Patent Publication Number: US-2023160816-A1

Title: Systems, methods, and computer program products for infrared imaging operations

Description:
TECHNOLOGICAL FIELD 
     Example embodiments of the present disclosure relate generally to imaging systems and, more particularly, to infrared (IR) imaging to detect and quantify gas leakages. 
     BACKGROUND 
     In many environments, such as manufacturing facilities, drilling locations, pipelines, and/or the like, gases may be used, stored, transferred, moved, etc. For example, a natural gas pipeline may transport natural gas (e.g., methane and/or the like) between locations. During transport, some gas may be emitted from such an example pipeline, such as due to a leak in the pipeline system (e.g., due to poor sealing at pipe junctions, an impact with the pipeline, etc.). In order to identify a leak and/or quantify the amount of gas emitted from the leak, hyperspectral cameras may be used. The inventors have identified numerous deficiencies with the existing technologies in this field, the remedies for which are the subject of the embodiments described herein. 
     BRIEF SUMMARY 
     As described above, many industries and environments rely upon or otherwise leverage gases in performing various operations associated with these industries. For example, the natural gas industry may extract, transport, and process natural gas (e.g., methane and/or the like) for subsequent use in generating heat, generating electricity, fueling vehicles, etc. The emittance of this gas to an external environment, such as due to a leak in one or more systems, may result in large costs in lost product as well as the potential for large fines from, for example, governmental regulatory agencies. Furthermore, the leakage of gases such as methane may present a dangerous condition to workers or otherwise impact workplace safety. As such, the accurate detection and quantification of gas leakages (e.g., a leaking plume of gas) is of critical importance in order to maximize profit while preventing hazardous conditions. 
     A key measurement or parameter in detecting or quantifying gas plumes, via image processing or the like, is the distance between the detection device (e.g., a hyperspectral camera or the like) and the leaking plume of gas. Traditional systems that attempt to determine this distance, however, are often rigid in that they are based upon an initial set up calibration procedure. For example, a hyperspectral camera may be placed at a particular location and positioned so as to capture images of particular areas at which gas may leak. A laser telemeter or rangefinder may be used to determine the distance between the hyperspectral camera and these areas of concern. In operation, however, the hyperspectral camera may be moved, the field of view (FOV) associated with one or more imaging devices of the camera may pivot, rotate, etc., and/or the gas leak may occur at any number of positions within the FOV of the camera, each of which may be associated with a different distance to the camera. As such, these conventional systems fail to provide dynamic calibrations and/or modification to gas leakage detection implementations by relying upon rigid calibration procedures that fail to account for the dynamic nature of real-world applications. 
     To solve these issues and others, example implementations of embodiments of the present disclosure may leverage a plurality of IR imaging devices configured to capture IR image data of respective FOVs. A computing device operably connected with these IR imaging devices may receive the respective IR image data and leverage a template matching procedure to determine respective features (e.g., central positions, edges, contours, corners, etc.) of a gas plume with the respective IR image data. A disparity between these features of the gas plume may be determined and, based upon this disparity, a distance between the imaging system and the gas plume may be determined, such as via epipolar geometry. In this way, the imaging system of the present disclosure may iteratively determine a distance between the imaging system and a detected gas plume so as to account for the varying environmental influences that impact operation of the imaging system. In doing so, the embodiments of the present disclosure may operate to improve gas leak detection and quantification while also providing mechanisms for modifying operation of the imaging system in response to environmental influences. 
     Apparatuses, methods, systems, devices, and associated computer program products are provided for IR image operations. An example imaging system may include a first IR imaging device configured to generate first IR image data of a field of view of the first IR imaging device and a second IR imaging device configured to generate second IR image data of a field of view of the second IR imaging device. The imaging system may further include a computing device operably connected with the first IR imaging device and the second IR imaging device. The computing device may be configured to receive the first IR image data from the first IR imaging device and receive the second IR image data from the second IR imaging device. The computing device may further determine, via a template matching procedure, a first feature representing a position of a gas plume based upon the first IR image data and a second feature representing a position of the gas plume based upon the second IR image data and determine a disparity between the first feature and the second feature. The computing device may also determine, via epipolar geometry, a distance between the imaging system and the gas plume based upon the determined disparity. 
     In some embodiments, the first IR imaging device and the second IR imaging device may be each supported by a housing such that a device spacing is defined between the first IR imaging device and the second IR imaging device. 
     In some further embodiments, the first IR imaging device and the second IR imaging device may be each associated with the same focal length and pixel size. 
     In some still further embodiments, the computing device may be configured to determine the distance between the imaging system and the gas plume based upon the determined disparity, the device spacing, the focal length, and the pixel size. 
     In some embodiments, the imaging system may further include a first filter attached to the first IR imaging device and a second filter attached to the second IR imaging device, wherein the first filter and the second filter each define a band-pass frequency associated with a frequency of the gas plume. 
     In some embodiments, the computing device, prior to determining the first feature of the gas plume and the second feature of the gas plume, may be further configured to generate template IR image data based upon the first IR image data, generate target IR image data based upon the second IR image data, and compare the similarity score with a template threshold. In an instance in which the similarity score satisfies the template threshold, the computing device may determine the first feature and the second feature. In an instance in which the similarity score fails to satisfy the template threshold, the computing device may generate an alert signal. 
     In some further embodiments, generating the template IR image data may include assigning a binary value to one or more data entries of the first IR image data. 
     In other further embodiments, generating the template IR image data may include assigning a binary value to one or more data entries of the second IR image data. 
     In some embodiments, the alert signal may be configured to present a notification to a user associated with the imaging system. 
     In other embodiments, the alert signal may be configured to set the distance between the imaging system and the gas plume as a default distance. 
     In some embodiments, the computing device may be configured to modify one or more operating parameters of the imaging system based upon the determined distance. 
     The above summary is provided merely for purposes of summarizing some example embodiments to provide a basic understanding of some aspects of the disclosure. Accordingly, it will be appreciated that the above-described embodiments are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. It will be appreciated that the scope of the disclosure encompasses many potential embodiments in addition to those here summarized, some of which will be further described below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Having described certain example embodiments of the present disclosure in general terms above, reference will now be made to the accompanying drawings. The components illustrated in the figures may or may not be present in certain embodiments described herein. Some embodiments may include fewer (or more) components than those shown in the figures. 
         FIG.  1 A  illustrates an example imaging system in operation with a target gas leak in accordance with some example embodiments described herein; 
         FIG.  1 B  illustrates another view of the example imaging system of  FIG.  1    with associated field of views (FOVs) shown, in accordance with some example embodiments described herein; 
         FIG.  1 C  illustrates an example visual representation of IR image data, such as generated by the imaging system of  FIG.  1   , in accordance with some example embodiments described herein; 
         FIG.  2    illustrates a schematic block diagram of example circuitry that may perform various operations, in accordance with some example embodiments described herein; 
         FIG.  3    illustrates an example flowchart for example distance determinations, in accordance with some example embodiments described herein; and 
         FIG.  4    illustrates an example flowchart for template threshold comparisons, in accordance with some example embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Some embodiments of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the disclosure are shown. Indeed, this disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like numbers refer to like elements throughout. As used herein, the description may refer to a computing device of an example imaging system as an example “apparatus.” However, elements of the apparatus described herein may be equally applicable to the claimed method and computer program product. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the present disclosure. 
     Definition of Terms 
     As used herein, the terms “data,” “content,” “information,” “electronic information,” “signal,” “command,” and similar terms may be used interchangeably to refer to data capable of being transmitted, received, and/or stored in accordance with embodiments of the present disclosure. Thus, use of any such terms should not be taken to limit the spirit or scope of embodiments of the present disclosure. Further, where a first device is described herein to receive data from a second device, it will be appreciated that the data may be received directly from the second device or may be received indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and/or the like, sometimes referred to herein as a “network.” Similarly, where a first device is described herein as sending data to a second device, it will be appreciated that the data may be sent directly to the second device or may be sent indirectly via one or more intermediary computing devices, such as, for example, one or more servers, remote servers, cloud-based servers (e.g., cloud utilities), relays, routers, network access points, base stations, hosts, and/or the like. 
     As used herein, the term “comprising” means including but not limited to and should be interpreted in the manner it is typically used in the patent context. Use of broader terms such as comprises, includes, and having should be understood to provide support for narrower terms such as consisting of, consisting essentially of, and comprised substantially of. 
     As used herein, the phrases “in one embodiment,” “according to one embodiment,” “in some embodiments,” and the like generally refer to the fact that the particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure. Thus, the particular feature, structure, or characteristic may be included in more than one embodiment of the present disclosure such that these phrases do not necessarily refer to the same embodiment. 
     As used herein, the word “example” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “example” is not necessarily to be construed as preferred or advantageous over other implementations. 
     As used herein, the terms “first IR imaging device” or “first IR imager” refer to a device or devices capable of generating first IR image data. Example first IR imaging devices may include a thermal imaging camera, an IR imager, an IR camera, a thermographic camera, and/or the like that may generate IR image data indicative of a field of view (FOV) of the first IR imaging device. Said differently, the first IR imaging device may include any device, apparatus, system, etc. capable of detecting infrared energy/radiation and converting said infrared energy/radiation into a corresponding electronic signal (e.g., first IR image data). By way of a non-limiting example, the first IR imaging device may include an IR camera configured to capture IR energy emitted by an example gas leakage source as described hereafter located within a first FOV associated with the first IR imaging device. The first IR imaging device may also be associated with a first filter that defines a first band-pass frequency (e.g., a device that passes frequencies within a certain range and attenuates frequencies outside this range). As described hereafter, this first filter may be configured to pass IR radiation having a frequency associated with the gas for which the imaging device is design to monitor (e.g., methane or the like) to the first IR imaging device. 
     As used herein, the terms “second IR imaging device” or “second IR imager” refer to a device or devices capable of generating second IR image data. Example second IR imaging devices may also include a thermal imaging camera, an IR imager, an IR camera, a thermographic camera, and/or the like that may generate IR image data indicative of a field of view (FOV) of the second IR imaging device. Said differently, the second IR imaging device may include any device, apparatus, system, etc. capable of detecting infrared energy/radiation and converting said infrared energy/radiation into a corresponding electronic signal (e.g., second IR image data). By way of a non-limiting example, the second IR imaging device may also include an IR camera configured to capture IR energy emitted by an example gas leakage source as described hereafter located within a second FOV associated with the second IR imaging device. The second IR imaging device may also be associated with a second filter that defines a second band-pass frequency (e.g., a device that passes frequencies within a certain range and attenuates frequencies outside this range). As described hereafter, this second filter may be configured to pass IR radiation having a frequency associated with the gas for which the imaging device is design to monitor (e.g., methane or the like) to the second IR imaging device and may further be configured for use with the same frequency as the first filter. Although described herein with refence to two (2) IR imaging devices, the present disclosure contemplates that the imaging system may include any number of IR imaging devices based upon the intended application of the imaging system. 
     As used herein, the term “computing device” refers to any user device, controller, object, or system which may be in physical or network communication with a first IR imaging device and the second IR imaging device as described hereafter. For example, the computing device may refer to a wireless electronic device configured to perform various IR image related operations in response to first IR image data and/or second IR image data generated by the first IR imaging device and the second IR imaging device, respectively. The computing device may be configured to communicate with the first IR imaging device and/or the second IR imaging device via Bluetooth, NFC, Wi-Fi, 3G, 4G, 5G protocols, and the like. In some instances, the computing device may comprise the first IR imaging device and/or the second IR imaging device (e.g., an integrated configuration). 
     As used herein, the terms “gas leak,” “gas plume,” and/or “gas leak plume” may refer to a collection of gas atoms or particles that include vast separation between individual atoms or particles. Such a gas may leak or otherwise be emitted from a containing vessel (e.g., natural gas pipeline or the like) and may be formed as a plume or column. This plume may be a vertical body of a first fluid (e.g., the leaking gas) moving relative or through another second fluid (e.g., the ambient air). As would be evident in light of the present disclosure, the intensity of the gas may dissipate as the distance between the leaking gas and the source of the leak increases. For example, a gas leak from a pipeline that contains methane gas may result in a gas plume of methane gas emitted from the pipeline such that the intensity (e.g., concentration) of methane gas decreases as the distance between the particles of methane gas and the location of the leakage increases. Although described herein with reference to an example methane gas application, the present disclosure contemplates that the imaging system(s) described herein may be configured for use with gas of any type, concentration, etc. 
     As used herein, the term “computer-readable medium” refers to non-transitory storage hardware, non-transitory storage device or non-transitory computer system memory that may be accessed by a computing device, a microcomputing device, a computational system or a module of a computational system to encode thereon computer-executable instructions or software programs. A non-transitory “computer-readable medium” may be accessed by a computational system or a module of a computational system to retrieve and/or execute the computer-executable instructions or software programs encoded on the medium. Exemplary non-transitory computer-readable media may include, but are not limited to, one or more types of hardware memory, non-transitory tangible media (for example, one or more magnetic storage disks, one or more optical disks, one or more USB flash drives), computer system memory or random access memory (such as, DRAM, SRAM, EDO RAM), and the like. 
     Having set forth a series of definitions called-upon throughout this application, an example system architecture and example apparatus is described below for implementing example embodiments and features of the present disclosure. 
     Device Architecture and Example Apparatus 
     With reference to  FIGS.  1 A- 1 B , an example imaging system  100  is illustrated with a first IR imaging device  102  and a second IR imaging device  104  operably coupled with a computing device  200  via a network  106 . As defined above, the first IR imaging device  102  may comprise a device capable of generating first IR image data and may be a thermal imaging camera, an IR imager, an IR camera, a thermographic camera, and/or the like. The first IR imaging device  102  may be associated with a FOV  103 . The FOV  103  may refer to the observable area within which the first IR imaging device  102  may capture images (e.g., generate first IR image data). As described hereafter, in some embodiments, the first IR imaging device  102  may be positioned or oriented such that a gas leakage source  10  is physically located within the FOV  103  of the first IR imaging device  102 . Said differently, the FOV  103  of the first IR imaging device  102  may be such that first IR image data generated by the first IR imaging device  102  (e.g., captured IR images of the FOV  103 ) may include IR image data indicative of or otherwise associated with the gas leakage source  10  (e.g., so as to capture IR image data of the gas plume  20 ). The present disclosure contemplates that the first IR imaging device  102  may be positioned at any physical location and at any orientation based upon the intended application of the system  100 . Furthermore, the present disclosure contemplates that the FOV  103  may be varied based upon the operating parameters of the first IR imaging device  102 . 
     As defined above, the second IR imaging device  104  may comprise a device capable of generating second IR image data and may be a thermal imaging camera, an IR imager, an IR camera, a thermographic camera, and/or the like. The second IR imaging device  104  may be associated with a FOV  105 . The FOV  105  may refer to the observable area within which the second IR imaging device  104  may capture images (e.g., generate second IR image data). As described hereafter, in some embodiments, the second IR imaging device  104  may be positioned or oriented such that a gas leakage source  10  is physically located within the FOV  105  of the second IR imaging device  104 . Said differently, the FOV  105  of the second IR imaging device  104  may be such that second IR image data generated by the second IR imaging device  104  (e.g., captured IR images of the FOV  105 ) may include IR image data indicative of or otherwise associated with the gas leakage source  10  (e.g., so as to capture IR image data of the gas plume  20 ). The present disclosure contemplates that the second IR imaging device  104  may be positioned at any physical location and at any orientation based upon the intended application of the system  100 . Furthermore, the present disclosure contemplates that the FOV  105  may be varied based upon the operating parameters of the second IR imaging device  104 . As would be evident in light of the present disclosure, the first FOV  103  and the second FOV  105  may be different based upon the different positions of the respective first IR imaging device  102  and second IR imaging device  104 . 
     In some embodiments as described herein, the first IR imaging device  102  and the second IR imaging device  104  may be formed as an integral device or may be otherwise commonly housed, such as via housing  108  of a hyperspectral camera. In such an embodiment, the FOV  103  and the FOV  105  may, for example, at least partially overlap. In such an embodiment, the first IR imaging device  102  and the second IR imaging device  104  may be positioned by the housing  108  such that a device spacing  110  is defined between these devices  102 ,  104  within the housing  108 . In other embodiments, the first IR imaging device  102  and the second IR imaging device  104  may be separately located. In such an embodiment, the device spacing  110  may refer to a linear distance between these separate housings (not shown). In any embodiment, the present disclosure contemplates that the FOV  103  and/or the FOV  105  may be dynamically adjusted (e.g., tilted, panned, pivoted, etc.) during performance of the operations described herein. Furthermore, the device spacing  110  may be known to or otherwise accessible by an example computing device of the present disclosure, such as to perform associated epipolar geometry determinations as described herein. 
     As described hereafter with reference to the operations of  FIGS.  3 - 4   , the imaging system  100  of the present disclosure may be positioned proximate a gas leakage source  10  (e.g., a pipeline or any feature, vessel, container, etc. from which gas may leak) so as to detect and quantify a gas plume  20  emitted from the gas leakage source  10 . The positioning may be such that a distance  112  exists between the gas leakage source  10  and, by association, the gas plume  20 . As shown in  FIGS.  1 B- 1 C , however, the first IR image data generated by the first IR imaging device  102  and the second IR image data generated by the second IR imaging device  104  may differ due to the different perspectives (e.g., FOVs  103 ,  105 ) of these devices  102 ,  104 . As shown in  FIG.  1 C , a visual representation of the first IR image data  114  and a visual representation of the second IR image data  116  illustrate that while the gas plume  20  is the same within each representation  114 ,  116 , the positioning of the gas plume  20  varies as described above. As such, a first feature (e.g., geometric center, center of mass, etc.)  118  or set of features (e.g., corners, edges, contours, etc.) for the gas plume  20  in the first IR image data  114  differs from a second feature (e.g., geometric center, center of mass, etc.)  120  or set of features (e.g., corners, edges, contours, etc.) for the same gas plume  20  in the second IR image data  116 . Any disparity between the first feature  118  of the gas plume  20  in the first IR image data  114  and the second feature  120  of the gas plume  20  in the second IR image data  116  may, as shown in  FIG.  1 B , be illustrated as a difference between x coordinates (i.e., x 1  and x 2 ). Said differently, the disparity described hereafter may, in some examples, refer to a difference between a measurement (x 1 ) from an edge of the FOV  103  of the first IR imaging device  102  and the center of the gas plume  20  and a measurement (x 2 ) from an edge of the FOV  105  of the second IR imaging device  104  and the center of the gas plume  20 . Although illustrated with reference to central locations of the gas plume  20 , first feature(s)  118  and second feature(s)  120  may refer to any location within the gas plume  20  based upon the intended application of the system  100 . 
     With continued reference to  FIGS.  1 A- 1 B , the imaging system  100  may include a computing device  200  that is connected with the first IR imaging device  102  and the second IR imaging device  104  over a network  106 . In some instances, the first IR imaging device  102  may comprise the computing device  200 , in whole or in part. In some instances, the second IR imaging device  104  may comprise the computing device  200 , in whole or in part. In other instances, the first IR imaging device  102 , the second IR imaging device  104 , and the computing device  200  may be formed as a single, integrated device. The computing device  200  may include circuitry, networked processors, or the like configured to perform some or all of the apparatus-based (e.g., IR image based) processes described herein and may be any suitable processing device and/or network server. In this regard, the computing device  200  may be embodied by any of a variety of devices. For example, the computing device  200  may be configured to receive/transmit data (e.g., IR image data) and may include any of a variety of fixed terminals, such as a server, desktop, or kiosk, or it may comprise any of a variety of mobile terminals, such as a portable digital assistant (PDA), mobile telephone, smartphone, laptop computer, tablet computer, or in some embodiments, a peripheral device that connects to one or more fixed or mobile terminals. Example embodiments contemplated herein may have various form factors and designs but will nevertheless include at least the components illustrated in  FIG.  2    and described in connection therewith. The computing device  200  may, in some embodiments, comprise several servers or computing devices performing interconnected and/or distributed functions. Despite the many arrangements contemplated herein, the computing device  200  is shown and described herein as a single computing device to avoid unnecessarily overcomplicating the disclosure. 
     The network  106  may include one or more wired and/or wireless communication networks including, for example, a wired or wireless local area network (LAN), personal area network (PAN), metropolitan area network (MAN), wide area network (WAN), or the like, as well as any hardware, software and/or firmware for implementing the one or more networks (e.g., network routers, switches, hubs, etc.). For example, the network  106  may include a cellular telephone, mobile broadband, long term evolution (LTE), GSM/EDGE, UMTS/HSPA, IEEE 802.11, IEEE 802.16, IEEE 802.20, Wi-Fi, dial-up, and/or WiMAX network. Furthermore, the network  106  may include a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed including, but not limited to TCP/IP based networking protocols. In some embodiments, the network  106  may refer to a collection of wired connections such that the first IR imaging device  102 , the second IR imaging device  104 , and/or the computing device  200  may be physically connected, via one or more networking cables or the like. 
     As illustrated in  FIG.  2   , the computing device  200  may include a processor  202 , a memory  204 , input/output circuitry  206 , and communications circuitry  208 . Moreover, the computing device  200  may include image processing circuitry  210  and/or machine learning circuitry  212 . The computing device  200  may be configured to execute the operations described below in connection with  FIGS.  3 - 4   . Although components  202  -  212  are described in some cases using functional language, it should be understood that the particular implementations necessarily include the use of particular hardware. It should also be understood that certain of these components  202 - 212  may include similar or common hardware. For example, two sets of circuitry may both leverage use of the same processor  202 , memory  204 , communications circuitry  208 , or the like to perform their associated functions, such that duplicate hardware is not required for each set of circuitry. The use of the term “circuitry” as used herein includes particular hardware configured to perform the functions associated with respective circuitry described herein. As described in the example above, in some embodiments, various elements or components of the circuitry of the computing device  200  may be housed within the first IR imaging device  102  and/or the second IR imaging device  104 . It will be understood in this regard that some of the components described in connection with the computing device  200  may be housed within one or more of the devices of  FIG.  1   , while other components are housed within another of these devices, or by yet another device not expressly illustrated in  FIG.  1   . 
     Of course, while the term “circuitry” should be understood broadly to include hardware, in some embodiments, the term “circuitry” may also include software for configuring the hardware. For example, although “circuitry” may include processing circuitry, storage media, network interfaces, input/output devices, and the like, other elements of the computing device  200  may provide or supplement the functionality of particular circuitry. 
     In some embodiments, the processor  202  (and/or co-processor or any other processing circuitry assisting or otherwise associated with the processor) may be in communication with the memory  204  via a bus for passing information among components of the computing device  200 . The memory  204  may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In other words, for example, the memory may be an electronic storage device (e.g., a non-transitory computer readable storage medium). The memory  204  may be configured to store information, data, content, applications, instructions, or the like, for enabling the computing device  200  to carry out various functions in accordance with example embodiments of the present disclosure. 
     The processor  202  may be embodied in a number of different ways and may, for example, include one or more processing devices configured to perform independently. Additionally or alternatively, the processor may include one or more processors configured in tandem via a bus to enable independent execution of instructions, pipelining, and/or multithreading. The use of the term “processing circuitry” may be understood to include a single core processor, a multi-core processor, multiple processors internal to the computing device, and/or remote or “cloud” processors. 
     In an example embodiment, the processor  202  may be configured to execute instructions stored in the memory  204  or otherwise accessible to the processor  202 . Alternatively or additionally, the processor  202  may be configured to execute hard-coded functionality. As such, whether configured by hardware or by a combination of hardware with software, the processor  202  may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly. Alternatively, as another example, when the processor  202  is embodied as an executor of software instructions, the instructions may specifically configure the processor  202  to perform the algorithms and/or operations described herein when the instructions are executed. 
     The computing device  200  further includes input/output circuitry  206  that may, in turn, be in communication with processor  202  to provide output to a user and to receive input from a user, user device, or another source. In this regard, the input/output circuitry  206  may comprise a display that may be manipulated by a mobile application. In some embodiments, the input/output circuitry  206  may also include additional functionality including a keyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, a microphone, a speaker, or other input/output mechanisms. The processor  202  and/or user interface circuitry comprising the processor  202  may be configured to control one or more functions of a display through computer program instructions (e.g., software and/or firmware) stored on a memory accessible to the processor (e.g., memory  204 , and/or the like). 
     The communications circuitry  208  may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and/or transmit data from/to a network and/or any other device, circuitry, or module in communication with the computing device  200 . In this regard, the communications circuitry  208  may include, for example, a network interface for enabling communications with a wired or wireless communication network. For example, the communications circuitry  208  may include one or more network interface cards, antennae, buses, switches, routers, modems, and supporting hardware and/or software, or any other device suitable for enabling communications via a network. Additionally or alternatively, the communication interface may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). These signals may be transmitted by the computing device  200  using any of a number of wireless personal area network (PAN) technologies, such as Bluetooth® v1.0 through v3.0, Bluetooth Low Energy (BLE), infrared wireless (e.g., IrDA), ultra-wideband (UWB), induction wireless transmission, or the like. In addition, it should be understood that these signals may be transmitted using Wi-Fi, Near Field Communications (NFC), Worldwide Interoperability for Microwave Access (WiMAX) or other proximity-based communications protocols. 
     The image processing circuitry  210  includes hardware components designed to analyze the first IR image data and/or the second IR image data so as to determine a first feature  118  of a gas plume  20  and a second feature  120  of the gas plume  20 . The image processing circuitry  210  may further determine a disparity between the first feature  118  and the second feature  120  as related to determination of a distance between the imaging system  100  and the gas plume  200 . Image processing circuitry  210  may utilize processing circuitry, such as the processor  202 , to perform its corresponding operations, and may utilize memory  204  to store collected information. In some instances, the image processing circuitry  210  may further include machine learning circuitry  212  that includes hardware components designed to leverage artificial intelligence to analyze the IR image data and/or compare the IR image data with one or more IR image templates (e.g., a comparison between template IR image data and target IR image data). By way of example, machine learning circuitry  212  may comprise or leverage an artificial neural network or convolutional neural network trained on at least image data of a plurality of captured IR image data associated with gas leaks or plumes. The machine learning circuitry  212  may also utilize processing circuitry, such as the processor  202 , to perform its corresponding operations, and may utilize memory  204  to store collected information. 
     It should also be appreciated that, in some embodiments, the image processing circuitry  210  and/or the machine learning circuitry  212  may include a separate processor, specially configured field programmable gate array (FPGA), or application specific interface circuit (ASIC) to perform its corresponding functions. In addition, computer program instructions and/or other type of code may be loaded onto a computer, processor or other programmable circuitry to produce a machine, such that the computer, processor other programmable circuitry that execute the code on the machine create the means for implementing the various functions, including those described in connection with the components of computing device  200 . 
     As described above and as will be appreciated based on this disclosure, embodiments of the present disclosure may be configured as apparatuses, systems, methods, and the like. Accordingly, embodiments may comprise various means including entirely of hardware or any combination of software with hardware. Furthermore, embodiments may take the form of a computer program product comprising instructions stored on at least one non-transitory computer-readable storage medium (e.g., computer software stored on a hardware device). Any suitable computer-readable storage medium may be utilized including non-transitory hard disks, CD-ROMs, flash memory, optical storage devices, or magnetic storage devices. 
     Example IR Image Operations 
       FIG.  3    illustrates a flowchart containing a series of operations for example distance determinations. The operations illustrated in  FIG.  3    may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., computing device  200 ), as described above. In this regard, performance of the operations may invoke one or more of processor  202 , memory  204 , input/output circuitry  206 , communications circuitry  208 , image processing circuitry  210 , and/or machine learning circuitry  212 . 
     As shown in operation  305 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for receiving first infrared (IR) image data from a first IR imaging device  102  associated with a field of view  103  of the first IR imaging device  102 . The first IR image data generated by the first IR imaging device  102  may include a plurality of data entries, one or more of which may be associated with particular pixels that represent the FOV  103  of the first IR imaging device  102 . For example, the first IR image data may be indicative of the intensity of the IR radiation received by the first IR imaging device  102  for each pixel captured for the FOV  103 . As described above, the first IR imaging device  102  may also be associated with a first filter that defines a first band-pass frequency (e.g., a device that passes frequencies within a certain range and attenuates frequencies outside this range). This first filter may be configured to pass IR radiation having a frequency associated with the gas for which the first IR imaging device  102  is design to monitor (e.g., methane or the like) to the first IR imaging device  102 . 
     In embodiments in which the computing device  200  and the first IR imaging device  102  are contained within a common device or integrated device (e.g., the computing device  200  comprises the first IR imaging device  102 ), the first IR image data may be received by the computing device  200  as part of normal operation of the first IR imaging device  102  (e.g., an internal transmission, if any). In other embodiments in which the computing device  200  is located separate from the first IR imaging device  102 , such as connected via network  106 , the computing device  200  may be configured to receive the first IR image data from the first IR imaging device  102  in response to generation of the first IR image data. Said differently, each instance of first IR image data generation may be transmitted to the computing device  200  upon generation. In other embodiments, the computing device  200  may periodically (e.g., according to a defined rate) request first IR image data from the first IR imaging device  102 . 
     In some embodiments, the first IR image data may be generated by the first IR imaging device  102  and/or transmitted to the computing device  200  in response to detection of a gas plume  20  within the FOV  103  of the first IR imaging device  102 . By way of example, the generation of the first IR image data may be responsive to a change in the IR radiation received by the first IR imaging device  102 , such as instances in which a gas plume  20  within the FOV  103  of the first IR imaging device  102  becomes present or becomes absent. Said differently, the first IR imaging device  102  may be configured to generate first IR image data in an instance in which the gas plume  20  is present within the FOV  103 . Furthermore, in some embodiments, the first IR imaging device  102  may continuously generate first IR image data, and, in response to a detection of a gas plume  20  or otherwise, the first IR imaging device  102  may transmit a request the first IR image data to the computing device  200 . 
     As shown in operation  310 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for receiving second infrared (IR) image data from the second IR imaging device  104  associated with a field of view  105  of the second IR imaging device  104 . The second IR image data generated by the second IR imaging device  104  may include a plurality of data entries, one or more of which may be associated with particular pixels that represent the FOV  105  of the second IR imaging device  104 . For example, the second IR image data may be indicative of the intensity of the IR radiation received by the second IR imaging device  104  for each pixel captured for the FOV  105 . As described above, the second IR imaging device  104  may also be associated with a second filter that defines a second band-pass frequency (e.g., a device that passes frequencies within a certain range and attenuates frequencies outside this range). This second filter may be configured to pass IR radiation having a frequency associated with the gas for which the second IR imaging device  104  is design to monitor (e.g., methane or the like) to the second IR imaging device  104 . 
     In embodiments in which the computing device  200  and the second IR imaging device  104  are contained within a common device or integrated device (e.g., the computing device  200  comprises the second IR imaging device  104  ), the second IR image data may also be received by the computing device  200  as part of normal operation of the second IR imaging device  104  (e.g., an internal transmission, if any). In other embodiments in which the computing device  200  is located separate from the second IR imaging device  104 , such as connected via network  106 , the computing device  200  may be configured to receive the second IR image data from the second IR imaging device  104  in response to generation of the second IR image data. Said differently, each instance of second IR image data generation may be transmitted to the computing device  200  upon generation. In other embodiments, the computing device  200  may periodically (e.g., according to a defined rate) request second IR image data from the second IR imaging device  104 . 
     In some embodiments, the second IR image data may be generated by the second IR imaging device  104  and/or transmitted to the computing device  200  in response to detection of a gas plume  20  within the FOV  105  of the second IR imaging device  104  and/or detection of a gas plume  20  within the FOV  103  (e.g., in response to detection by the first IR imaging device  102  ). By way of example, the generation of the second IR image data may be responsive to a change in the IR radiation received by the second IR imaging device  104 , such as instances in which a gas plume  20  within the FOV  105  of the second IR imaging device  104  becomes present or becomes absent. Said differently, the second IR imaging device  104  may be configured to generate second IR image data in an instance in which the gas plume  20  is present within the FOV  105 . Furthermore, in some embodiments, the second IR imaging device  104  may continuously generate second IR image data, and, in response to a detection of a gas plume  20  or otherwise, the second IR imaging device  104  may transmit a request the second IR image data to the computing device  200 . 
     As shown in operation  315 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , image processing circuitry  210 , machine learning circuitry  212 , or the like, for determining, via a template matching procedure, a first feature  118  representing a position of a gas plume  20  based upon the first IR image data and a second feature  120  representing a position of the gas plume  20  based upon the second IR image data As described hereafter with reference to  FIG.  4   , the computing device  200  may, via a training procedure, calibration procedure, or the like, analyze IR image data from a plurality of different gases so as to determine if the intensity of the IR radiation for this IR image data is indicative of the presence of a gas plume and/or the concentration of the gas in the gas plume. As such, at operation  315 , the computing device  200  may, for example, generate template IR image data based upon the first IR image data in which a binary score is applied (e.g., either a zero (0) or a one (1)) to each pixel within the first IR image data. By way of a particular example, if the comparison between a particular pixel within the first IR image data is sufficiently similar to one or more instances of calibrated IR image data (e.g., calibrated data of known gas plume intensities, concentrations, etc.), the computing device  200  may assign a one (1) to that particular pixel of the first IR image data. This process may be iteratively performed for each pixel within the first IR image data in order to determine which pixels within the IR image data are associated with the gas plume  20  so as to generate template IR image data. Following this generation, the computing device  200  generate target IR image data based upon the second IR image data (e.g., data that may be compared against the template IR image data). As described hereafter, the target IR data may comprise a collection of IR image data entries from the second IR image data that may be compared against at least a portion of the template IR image data in order to determine the presence of the gas plume  20  within these pixels of the second IR image data. 
     Thereafter, the computing device  200  may leverage one or more techniques for determining the geometric center, mass center, corner(s), edge(s), contour(s), etc. of the gas plume  20  based upon the first IR image data (e.g., the first feature  118 ) and the geometric center, mass center, corner(s), edge(s), contour(s), etc. of the gas plume  20  based upon the second IR image data (e.g., the second feature  120 ). In some embodiments. The determined first feature  118  and the determined second feature  120  may be coordinates for the respective central positions  118 ,  120  in two-dimensional space as described above. Again, the present disclosure contemplates that the first feature  118  and the second feature  120  may refer to any location within the gas plume  20  based upon the intended application of the system  100 . 
     Thereafter, as shown in operation  320 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , image processing circuitry  210 , machine learning circuitry  212 , or the like, for determining a disparity between the first feature  118  and the second feature  120 . As described above, the first feature  118  and the second feature  120  determined at operation  315  may be coordinates in two-dimensional space (e.g., (x, y) coordinates). As such, the disparity at operation  320  may refer to the mathematical difference between the first coordinates for the first feature  118  and the second coordinates for the second feature  120 . For example, the Euclidean distance between the coordinates may be used to determine the disparity at operation  320 . Given that the housing  108  may constrain the physical position of the first IR imaging device  102  and the second IR imaging device  104 , the disparity determination at operation  320  may, in some embodiments, rely upon a single coordinate (e.g., an x coordinate) from the coordinates associated with the first feature  118  and the second feature  120  (e.g., a difference between the x coordinates). As such, the disparity determined at operation  320  may refer to a single value that is the mathematical difference between the x coordinate (e.g., x 1  in  FIG.  1 B ) of the first feature  118  and the x coordinate (e.g., x 2  in  FIG.  1 B ) of the second feature  120 . Although described herein with reference to a disparity determination based upon the x coordinates, the present disclosure contemplates that this disparity determination may similarly be performed for each coordinate in the coordinate pair (e.g., y 1  and y 2 ). In some embodiments, the determination of the disparity at operation  320  may be performed as part of operation  325  as described hereafter. 
     Thereafter, as shown in operation  325 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , image processing circuitry  210 , machine learning circuitry  212 , or the like, for determining, via epipolar geometry, a distance  112  between the imaging system  100  and the gas plume  20  based upon the determined disparity. As would be evident in light of the present disclosure, epipolar geometry is the geometry of stereo vision in which two cameras (e.g., first IR imaging device  102  and second IR imaging device  104 ) capture a two-dimensional scene from distinct positions (e.g., FOV  103  and FOV  105 , respectively). The geometric relations between these two-dimensional points (e.g., the coordinates of the first feature  118  and the second feature  120 ) and their projections in two-dimensional space may be used to determine a distance between the gas plume  20  and the imaging system  100 . As shown in the equation below, the disparity may be simplified to the absolute value of the difference between the x coordinates of the first feature  118  and the second feature  120  as described above with reference to operation  320 . As above, the present disclosure contemplates that performance of operation  325  may be based upon the absolute value of the difference between any and/or all of the coordinate pairs (e.g., y 1  and y 2 ). The distance D (e.g., distance  112 ) may be determined by this equation where B is the device spacing  110 , f is the focal length of the first IR imaging device  102  and the focal length of the second IR imaging device  104 , and ps is the pixel size of the first IR imaging device  102  and the pixel size of the second IR imaging device  104 . The focal length and/or pixel size for each of the first IR imaging device  102  and/or the second IR imaging device  104  may be known to or otherwise accessible by the computing device  200  (e.g., set parameters of the first IR imaging device  102  and/or the second IR imaging device  104 ). As such, epipolar geometry, such as via the equation produced below, may be used to determine the distance  112  between the imaging system  100  and the gas plume  20 . 
     
       
         
           
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     In some embodiments, as shown in operation  330 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , image processing circuitry  210 , machine learning circuitry  212 , or the like, for modifying one or more operating parameters of the imaging system  100  based upon the determined distance (e.g., distance D or  112  in operation  325 ). As described above, the embodiments of the present disclosure operate to provide an imaging system that dynamically modifies operation in response to environmental conditions or influences. As such, in some embodiments, the distance determination at operation  325  may, for example, indicate that the imaging system  110  has rotated or otherwise moved such that the ability to accurate capture IR image data of the gas plume  20  is reduced or impeded. As such, the imaging system  100  may, for example, modify an operating parameter of either the first IR imaging device  102  or the second IR imaging device  104  by rotating, tilting, or otherwise moving the first IR imaging device  102  and/or the second IR imaging device  104  in response to the determined distance. Although described herein with modification of a physical position of an element of the imaging system  100 , the present disclosure contemplates that the one or more operating parameters may refer to any condition, output, timing, position, etc. of any component of the imaging system  100  based upon the nature of the determined distance. 
       FIG.  4    illustrates a flowchart containing a series of operations for template threshold comparisons. The operations illustrated in  FIG.  4    may, for example, be performed by, with the assistance of, and/or under the control of an apparatus (e.g., computing device  200 ), as described above. In this regard, performance of the operations may invoke one or more of processor  202 , memory  204 , input/output circuitry  206 , communications circuitry  208 , image processing circuitry  210 , and/or machine learning circuitry  212 . 
     As shown in operation  405 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for generating template IR image data based upon the first IR image data. As described above, the computing device  200  may, for example, apply a binary score (e.g., either a zero (0) or a one (1)) to each pixel (e.g., each first IR image data entry) within the first IR image data so as to generate template IR image data against which IR image from the second IR image device may be compared as described hereafter. By way of continued example, if the comparison between a particular pixel within the first IR image data is sufficiently similar to one or more calibrated IR image data entries (e.g., calibrated values of known gas plume intensities, concentrations, etc.), the computing device  200  may assign a one (1) to that particular pixel. This process may be iteratively performed for each pixel within the first IR image data in order to determine the pixels within the first IR image data are associated with the gas plume  20  and to generate template IR image data. Although described herein with reference to a binary value assignment procedure, the present disclosure contemplates that the computing device  200  may leverage any mechanism, machine learning model, artificial intelligence technique, or the like to generate template IR image data based upon the first IR image data. 
     As shown in operation  410 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for generating target IR image data based upon the second IR image data. As described above with reference to operation  315 , the computing device  200  may, generate target IR image data based upon the second IR image data (e.g., data that may be compared against the template IR image data generate at operation  405 ). As described herein, the target IR data may comprise a collection of IR image data entries from the second IR image data that may be compared against at least a portion of the template IR image data in order to determine the presence of the gas plume  20  within these pixels of the second IR image data (e.g., generate a similarity score). Although described herein as a collection of second IR image data entries, the present disclosure again contemplates that the computing device  200  may leverage any mechanism, machine learning model, artificial intelligence technique, or the like to select second IR image data entries for generating target IR image data. Although described herein with reference to use of the first IR image data in generating the template IR image data at operation  405  and use of the second IR image data in generating the target IR image data at operation  410 , the present disclosure contemplates that, in some embodiments, the second IR image data may be used to generate the template IR image data and the first IR image data may be used to generate the target IR image data. 
     Thereafter, as shown in operations  415 ,  420  the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for generating a similarity score based upon a comparison between the template IR image data and the target IR image data and comparing the similarity score with a template threshold. By way of continued example, the first IR image device  102  and the second IR image device  104  may iteratively generate first and second IR image data, respectively, that may or may not include pixels associated with gas plumes or other gas leakages within their respective FOVs  103 ,  105 . As such, the template threshold comparisons at operations  415 ,  420  may operate to prevent unnecessary processing in instances in which the IR image data of either the first IR imaging device  102  or the second IR imaging device  104  fails to include sufficient pixels associated with detection of a gas plume  20 . As such, the comparison at operation  415  may refer to a comparison between the template IR image data and the target IR image data to determine a similarity score. The similarity score may refer to a pixel-by-pixel (e.g., data entry by data entry) comparison between the binary values assigned to each of the pixels within the first IR image data and the corresponding pixels within the target IR image data generated based upon the second IR image data. This similarity score may, in some embodiments provide a mathematical value indicative of the similarity between the template IR image data and the target IR image data. 
     Thereafter, as shown in operation  420 , the similarity score may be compared with a template threshold to determine if the similarity score satisfies or exceeds a minimum value (e.g., a minimum number of pixels assigned a one (1)). By way of a nonlimiting example, the template threshold may define a value of 50% in that at least 50% of the pixels in the target IR image data substantially match the corresponding pixels in the template IR image data. In an instance in which the similarity satisfies the template threshold, as shown in operation  425 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for determining the first feature  118  and the second feature  120  as described above with reference to  FIG.  3   . 
     In an instance in which the similarity score fails to satisfy the template threshold, as shown in operation  430 , the apparatus (e.g., computing device  200 ) includes means, such as processor  202 , communications circuitry  208 , image processing circuitry  210 , or the like, for generating an alert signal. By way of example, in some embodiments the alert signal may cause the computing device  200  to generate a user notification for presenting to a user associated with the imaging system  100 . For example, a display communicably coupled with the computing device  200  may receive a user notification and/or an alert signal comprising instructions to display the user notification and may subsequently display this notification to the user for review. In other embodiments, the alert signal may be configured to set the distance between the imaging system  100  and the gas plume  20  as a default distance. By way of example, in some instances the first IR image data and/or the second IR image data may fail to include sufficient IR image data associated with the gas plume  20  to allow for determination of the distance  112  between the imaging system  100  and the gas plume  20 . In such an embodiment, the computing device  200  may set the distance as a default distance, such as the distance determine by an initial calibration procedure, the limit of the imaging system&#39;s resolution, and/or the like until further iterations provide sufficient IR image data to perform the operations of  FIG.  3   . 
       FIGS.  3 - 4    thus illustrate flowcharts describing the operation of apparatuses, methods, and computer program products according to example embodiments contemplated herein. It will be understood that each flowchart block, and combinations of flowchart blocks, may be implemented by various means, such as hardware, firmware, processor, circuitry, and/or other devices associated with execution of software including one or more computer program instructions. For example, one or more of the operations described above may be implemented by an apparatus executing computer program instructions. In this regard, the computer program instructions may be stored by a memory  204  of the computing device  200  and executed by a processor  202  of the computing device  200 . 
     As will be appreciated, any such computer program instructions may be loaded onto a computer or other programmable apparatus (e.g., hardware) to produce a machine, such that the resulting computer or other programmable apparatus implements the functions specified in the flowchart blocks. These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture, the execution of which implements the functions specified in the flowchart blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions executed on the computer or other programmable apparatus provide operations for implementing the functions specified in the flowchart blocks. 
     The flowchart blocks support combinations of means for performing the specified functions and combinations of operations for performing the specified functions. It will be understood that one or more blocks of the flowcharts, and combinations of blocks in the flowcharts, can be implemented by special purpose hardware-based computer systems which perform the specified functions, or combinations of special purpose hardware with computer instructions.