Patent Publication Number: US-2023153987-A1

Title: Object defect detection

Description:
BACKGROUND 
     During manufacturing or operation of products, defects can occur. For example, during manufacturing of a product that includes screws (e.g., screw used to secure parts of a computer together), a screw may become stripped during installation of the screw in the product. In another example, a cable that is to plug into a connector may experience an installation defect where the cable is not fully plugged into the connector. It may be impossible to manually inspect each product in order to identify defects, especially when there may be a large number of products being manufactured. 
     SUMMARY 
     In accordance with the present disclosure, one or more computing devices and/or methods for object defect detection are provided. A segmentation model may be trained to determine whether an object, depicted within images, has a defect. The defect may correspond to a stripped screw, a cable that is not fully connected, a chip, a dent, missing material, a missing part, a label or part that is not aligned or installed in the correct location, or other type of defect. In some embodiments, the segmentation model may be trained using sparse training data of training images labeled as either having the defect or not having the defect. In some embodiments, the sparse training data may have less than a threshold amount of training images (e.g., less than a thousand or any other number of training images labeled as depicting a defective or non-defective object). In some embodiments, image augmentation may be applied to the training images and/or the segmentation in order to generate augmented training images and/or segmentations that may be used to improve the accuracy of the segmentation model notwithstanding the relatively sparse/small amount of initial training data (e.g., a hundred training images may be transformed into thousands of augmented training images). 
     Once trained, the segmentation model may evaluate images to determine whether the images depict objects with defects for not. For example, an image may depict an object for evaluation as to whether the object has a defect, such as whether the image depicts a screw with a stripped screw head. Accordingly, the image may be input into the segmentation model. A screw head of the screw may be an object region of interest for determining whether the screw has a defect of a stripped screw head. The segmentation model may make a prediction for each pixel in the image as to whether the pixel is part of the object region of interest, such as whether each pixel depicts the screw head of the screw. In this way, a segmentation mask or other type of output is created to identify the pixels that are predicted by the segmentation model as being part of the object region of interest, such as pixels depicting the screw head. 
     Post processing is applied to the output of the segmentation model such as upon the segmentation mask identifying the object region of interest. An object region area may be calculated for the object region of interest. In some embodiments, the object region area may correspond to a number of pixels comprised within the object region of interest identified by the segmentation model (e.g., an area or number of pixels labeled by the segmentation model as being part of the screw head). A convex hull encompassing the object region of interest may be identified. The convex hull may be a shape just large enough to encompass the object region of interest without cutting into the object region of interest. A convex hull area of the convex hull may be calculated. In some embodiments, the convex hull area may correspond to a number of pixels comprised within the convex hull. A ratio of the object region area to the convex hull area may be calculated. The ratio may be compared to a threshold to determine whether the object has the defect or not. In an example, if the ratio indicates that the object region area is a threshold amount similar to the convex hull area, then the screw head may be determined as having the defect of being a stripped screw head that is relatively convex (e.g., a stripped screw head is relatively convex). If the ratio indicates that the object region area is not a threshold amount similar to the convex hull area, then the screw head may be determined as not having the defect of being a stripped screw head because the screw head is relatively non-convex (e.g., a cross shape is relatively non-convex). 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       While the techniques presented herein may be embodied in alternative forms, the particular embodiments illustrated in the drawings are only a few examples that are supplemental of the description provided herein. These embodiments are not to be interpreted in a limiting manner, such as limiting the claims appended hereto. 
         FIG.  1    is an illustration of a scenario involving various examples of networks that may connect servers and clients. 
         FIG.  2    is an illustration of a scenario involving an example configuration of a server that may utilize and/or implement at least a portion of the techniques presented herein. 
         FIG.  3    is an illustration of a scenario involving an example configuration of a client that may utilize and/or implement at least a portion of the techniques presented herein. 
         FIG.  4    is a flow chart illustrating an example method for object defect detection. 
         FIG.  5    is a component block diagram illustrating an example system for training a segmentation model. 
         FIG.  6    is a component block diagram illustrating an example system for object defect detection of a screw head. 
         FIG.  7    is a component block diagram illustrating an example system for object defect detection of a screw head. 
         FIG.  8    is a component block diagram illustrating an example system for object defect detection of a cable and connector. 
         FIG.  9    is a component block diagram illustrating an example system for object defect detection. 
         FIG.  10    is an illustration of a scenario featuring an example non-transitory machine readable medium in accordance with one or more of the provisions set forth herein. 
     
    
    
     DETAILED DESCRIPTION 
     Subject matter will now be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific example embodiments. This description is not intended as an extensive or detailed discussion of known concepts. Details that are known generally to those of ordinary skill in the relevant art may have been omitted, or may be handled in summary fashion. 
     The following subject matter may be embodied in a variety of different forms, such as methods, devices, components, and/or systems. Accordingly, this subject matter is not intended to be construed as limited to any example embodiments set forth herein. Rather, example embodiments are provided merely to be illustrative. Such embodiments may, for example, take the form of hardware, software, firmware or any combination thereof. 
     1. Computing Scenario 
     The following provides a discussion of some types of computing scenarios in which the disclosed subject matter may be utilized and/or implemented. 
     1.1. Networking 
       FIG.  1    is an interaction diagram of a scenario  100  illustrating a service  102  provided by a set of servers  104  to a set of client devices  110  via various types of networks. The servers  104  and/or client devices  110  may be capable of transmitting, receiving, processing, and/or storing many types of signals, such as in memory as physical memory states. 
     The servers  104  of the service  102  may be internally connected via a local area network  106  (LAN), such as a wired network where network adapters on the respective servers  104  are interconnected via cables (e.g., coaxial and/or fiber optic cabling), and may be connected in various topologies (e.g., buses, token rings, meshes, and/or trees). The servers  104  may be interconnected directly, or through one or more other networking devices, such as routers, switches, and/or repeaters. The servers  104  may utilize a variety of physical networking protocols (e.g., Ethernet and/or Fiber Channel) and/or logical networking protocols (e.g., variants of an Internet Protocol (IP), a Transmission Control Protocol (TCP), and/or a User Datagram Protocol (UDP). The local area network  106  may include, e.g., analog telephone lines, such as a twisted wire pair, a coaxial cable, full or fractional digital lines including T1, T2, T3, or T4 type lines, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, or other communication links or channels, such as may be known to those skilled in the art. The local area network  106  may be organized according to one or more network architectures, such as server/client, peer-to-peer, and/or mesh architectures, and/or a variety of roles, such as administrative servers, authentication servers, security monitor servers, data stores for objects such as files and databases, business logic servers, time synchronization servers, and/or front-end servers providing a user-facing interface for the service  102 . 
     Likewise, the local area network  106  may comprise one or more sub-networks, such as may employ different architectures, may be compliant or compatible with differing protocols and/or may interoperate within the local area network  106 . Additionally, a variety of local area networks  106  may be interconnected; e.g., a router may provide a link between otherwise separate and independent local area networks  106 . 
     In scenario  100  of  FIG.  1   , the local area network  106  of the service  102  is connected to a wide area network  108  (WAN) that allows the service  102  to exchange data with other services  102  and/or client devices  110 . The wide area network  108  may encompass various combinations of devices with varying levels of distribution and exposure, such as a public wide-area network (e.g., the Internet) and/or a private network (e.g., a virtual private network (VPN) of a distributed enterprise). 
     In the scenario  100  of  FIG.  1   , the service  102  may be accessed via the wide area network  108  by a user  112  of one or more client devices  110 , such as a portable media player (e.g., an electronic text reader, an audio device, or a portable gaming, exercise, or navigation device); a portable communication device (e.g., a camera, a phone, a wearable or a text chatting device); a workstation; and/or a laptop form factor computer. The respective client devices  110  may communicate with the service  102  via various connections to the wide area network  108 . As a first such example, one or more client devices  110  may comprise a cellular communicator and may communicate with the service  102  by connecting to the wide area network  108  via a wireless local area network  106  provided by a cellular provider. As a second such example, one or more client devices  110  may communicate with the service  102  by connecting to the wide area network  108  via a wireless local area network  106  provided by a location such as the user&#39;s home or workplace (e.g., a WiFi (Institute of Electrical and Electronics Engineers (IEEE) Standard 802.11) network or a Bluetooth (IEEE Standard 802.15.1) personal area network). In this manner, the servers  104  and the client devices  110  may communicate over various types of networks. Other types of networks that may be accessed by the servers  104  and/or client devices  110  include mass storage, such as network attached storage (NAS), a storage area network (SAN), or other forms of computer or machine readable media. 
     1.2. Server Configuration 
       FIG.  2    presents a schematic architecture diagram  200  of a server  104  that may utilize at least a portion of the techniques provided herein. Such a server  104  may vary widely in configuration or capabilities, alone or in conjunction with other servers, in order to provide a service such as the service  102 . 
     The server  104  may comprise one or more processors  210  that process instructions. The one or more processors  210  may optionally include a plurality of cores; one or more coprocessors, such as a mathematics coprocessor or an integrated graphical processing unit (GPU); and/or one or more layers of local cache memory. The server  104  may comprise memory  202  storing various forms of applications, such as an operating system  204 ; one or more server applications  206 , such as a hypertext transport protocol (HTTP) server, a file transfer protocol (FTP) server, or a simple mail transport protocol (SMTP) server; and/or various forms of data, such as a database  208  or a file system. The server  104  may comprise a variety of peripheral components, such as a wired and/or wireless network adapter  214  connectible to a local area network and/or wide area network; one or more storage components  216 , such as a hard disk drive, a solid-state storage device (SSD), a flash memory device, and/or a magnetic and/or optical disk reader. 
     The server  104  may comprise a mainboard featuring one or more communication buses  212  that interconnect the processor  210 , the memory  202 , and various peripherals, using a variety of bus technologies, such as a variant of a serial or parallel AT Attachment (ATA) bus protocol; a Uniform Serial Bus (USB) protocol; and/or Small Computer System Interface (SCI) bus protocol. In a multibus scenario, a communication bus  212  may interconnect the server  104  with at least one other server. Other components that may optionally be included with the server  104  (though not shown in the schematic architecture diagram  200  of  FIG.  2   ) include a display; a display adapter, such as a graphical processing unit (GPU); input peripherals, such as a keyboard and/or mouse; and a flash memory device that may store a basic input/output system (BIOS) routine that facilitates booting the server  104  to a state of readiness. 
     The server  104  may operate in various physical enclosures, such as a desktop or tower, and/or may be integrated with a display as an “all-in-one” device. The server  104  may be mounted horizontally and/or in a cabinet or rack, and/or may simply comprise an interconnected set of components. The server  104  may comprise a dedicated and/or shared power supply  218  that supplies and/or regulates power for the other components. The server  104  may provide power to and/or receive power from another server and/or other devices. The server  104  may comprise a shared and/or dedicated climate control unit  220  that regulates climate properties, such as temperature, humidity, and/or airflow. Many such servers  104  may be configured and/or adapted to utilize at least a portion of the techniques presented herein. 
     1.3. Client Device Configuration 
       FIG.  3    presents a schematic architecture diagram  300  of a client device  110  whereupon at least a portion of the techniques presented herein may be implemented. Such a client device  110  may vary widely in configuration or capabilities, in order to provide a variety of functionality to a user such as the user  112 . The client device  110  may be provided in a variety of form factors, such as a desktop or tower workstation; an “all-in-one” device integrated with a display  308 ; a laptop, tablet, convertible tablet, or palmtop device; a wearable device mountable in a headset, eyeglass, earpiece, and/or wristwatch, and/or integrated with an article of clothing; and/or a component of a piece of furniture, such as a tabletop, and/or of another device, such as a vehicle or residence. The client device  110  may serve the user in a variety of roles, such as a workstation, kiosk, media player, gaming device, and/or appliance. 
     The client device  110  may comprise one or more processors  310  that process instructions. The one or more processors  310  may optionally include a plurality of cores; one or more coprocessors, such as a mathematics coprocessor or an integrated graphical processing unit (GPU); and/or one or more layers of local cache memory. The client device  110  may comprise memory  301  storing various forms of applications, such as an operating system  303 ; one or more user applications  302 , such as document applications, media applications, file and/or data access applications, communication applications such as web browsers and/or email clients, utilities, and/or games; and/or drivers for various peripherals. The client device  110  may comprise a variety of peripheral components, such as a wired and/or wireless network adapter  306  connectible to a local area network and/or wide area network; one or more output components, such as a display  308  coupled with a display adapter (optionally including a graphical processing unit (GPU)), a sound adapter coupled with a speaker, and/or a printer; input devices for receiving input from the user, such as a keyboard  311 , a mouse, a microphone, a camera, and/or a touch-sensitive component of the display  308 ; and/or environmental sensors, such as a global positioning system (GPS) receiver  319  that detects the location, velocity, and/or acceleration of the client device  110 , a compass, accelerometer, and/or gyroscope that detects a physical orientation of the client device  110 . Other components that may optionally be included with the client device  110  (though not shown in the schematic architecture diagram  300  of  FIG.  3   ) include one or more storage components, such as a hard disk drive, a solid-state storage device (SSD), a flash memory device, and/or a magnetic and/or optical disk reader; and/or a flash memory device that may store a basic input/output system (BIOS) routine that facilitates booting the client device  110  to a state of readiness; and a climate control unit that regulates climate properties, such as temperature, humidity, and airflow. 
     The client device  110  may comprise a mainboard featuring one or more communication buses  312  that interconnect the processor  310 , the memory  301 , and various peripherals, using a variety of bus technologies, such as a variant of a serial or parallel AT Attachment (ATA) bus protocol; the Uniform Serial Bus (USB) protocol; and/or the Small Computer System Interface (SCI) bus protocol. The client device  110  may comprise a dedicated and/or shared power supply  318  that supplies and/or regulates power for other components, and/or a battery  304  that stores power for use while the client device  110  is not connected to a power source via the power supply  318 . The client device  110  may provide power to and/or receive power from other client devices. 
     2. Presented Techniques 
     One or more systems and/or techniques for object defect identification are provided. Objects may have various types of defects, which may occur during manufacturing or use of the objects. For example, a screw may have a stripped screw head, a cable may have an install defect of not being properly connected to a connector, a label may be incorrectly positioned on a product, a toy may have a crack or dent, a product may have a missing part, etc. If there is a large number of objects to inspect for defects such as during manufacturing of the objects, then it may be impractical for humans to manually inspect the objects for defects. 
     A machine learning model may be trained to label images as either depicting an object with a defect or depicting an object without the defect. Classification machine learning models may require thousands of training images manually labeled as depicting an object with or without the defect in order to accurately determine whether input images depict the object with or without the defect. Unfortunately, there may only be a sparse training set of training images for training a classification machine learning model (e.g., 100 images and only 15 images depict a screw with a stripped screw head). Thus, the classification machine learning model cannot be adequately trained using the sparse training set of training images to accurately determine whether input images depict the object with or without the defect. For example, the classification machine learning model may merely memorize the 15 images depicting the screw with the stripped screw head, and thus suffers from overfitting. Additionally, manually acquiring and labeling thousands of images of screws with and without stripped screw heads may be impractical. 
     Accordingly, as provided herein, a segmentation model and post processing are used to determine whether images depict an object with a defect or without the defect. In some embodiments, the segmentation model may be trained on a sparse training set of training images labeled as either depicting an object with the defect or within the defect (e.g., less than a threshold number of training images such as less than a thousand training images, less than a few hundred training images, or any other number of training images). In some embodiments, the segmentation model may be trained on any number of training images, as is not limited to being trained on a sparse training set of training images. In some embodiments, the segmentation model is trained to identify an object region of interest of the object, such as to output predictions for each pixel within an image as to whether each pixel is part of the object region of interest (e.g., part of a screw head of the screw) or not part of the object region of interested (e.g., not part of the screw head). 
     Once the segmentation model has identified the object region of interest in the image, then post processing is performed to transform the output of the segmentation model (e.g., a segmentation mask indicating which pixels are part of the screw head and which pixels are not) into a label as to whether the object depicts a screw head with or without a stripped screw head. The post processing may calculate an object region area for the object region of interest. In some embodiments, the object region area may correspond to a number of pixels comprised within the object region of interest identified by the segmentation model (e.g., an area of pixels labeled by the segmentation model as being part of the screw head). The post processing may identify a convex hull encompassing the object region of interest, such as by using image processing functionality. The post processing may calculate a convex hull area of the convex hull. In some embodiments, the convex hull area may correspond to a number of pixels comprised within the convex hull. The post processing may calculate a ratio of the object region area to the convex hull area. 
     The post processing may compare the ratio to a threshold to determine whether the object has the defect or not. In an example, if the ratio indicates that the object region area is a threshold amount similar to the convex hull area, then the screw head has the defect of being a stripped screw head that is relatively convex (e.g., a stripped screw head is relatively convex). If the ratio indicates that the object region area is not a threshold amount similar to the convex hull area, then the screw head does not the defect of being a stripped screw head because the screw head is relatively non-convex (e.g., a cross shape of a non-stripped screw head is relatively non-convex). 
     Various actions may be triggered if a defect is detected. For example, a description of the defect may be generated and displayed on a computing device. In another example, instructions for how to address/fix the defect may be generated and displayed on the computing device (e.g., instructions for how to remove and replace a screw with a stripped screw head, instructions for how to connect a cable that is not fully plugged in, instructions for locating and replacing/installing a missing part, instructions to discard a dented object, instructions for where to reinstall a misaligned label, etc.). In another example, the object may be marked as defective and replacement of the object may be facilitated. In another example, a machine (e.g., a factory machine such as a robotic arm) is controlled to discard the object, replace the object with a replacement object, or correct the defect, such as where commands are sent to the machine to remove and replace the defective screw, realign a part, etc. 
     One embodiment of object defect detection is illustrated by an exemplary method  400  of  FIG.  4    and is further described in conjunction with systems  500 ,  600 ,  700 ,  800 , and  900  of  FIGS.  5 - 9   . The method  400  may utilize a segmentation model  508  of a defect detection system  506  for object defect detection.  FIG.  5    illustrates the segmentation model  508  of the defect detection system  506  being trained by a model training process  504  in order to predict whether pixels are part of an object region of interest of an object having a defect or not having a defect. In some embodiments, the model training process  504  may perform a binary segmentation technique to train the segmentation model  508  using sparse training data  502  of training images labeled as either having the defect or not having the defect within the object region of interest. 
     Because the sparse training data  502  may have a limited number of training images (e.g., as opposed to thousands of training images that could be used to train a classification machine learning model), image augmentation  510  may be implemented by the modeling training process  504  in order to generate augmented training images for further training the segmentation model  508 . For example, the image augmentation  510  may be performed upon a training image in order to rotate the training image, crop the training image, move the training image around, and/or perform a variety of other modifications to the training image in order to generate a plurality of augmented training images. This may be performed for each of the training images within the sparse training data  502  in order to generate a significant number of augmented training images for further training the segmentation model  508  for making accurate predictions as to whether pixels of an input image are part of an object region of interest of an object with a defect or without a defect. The image augmentation  510  may also be performed upon ground truth segmentation masks or other output or segmentation of the segmentation model  508  in order to further train the segmentation model  508 . In some embodiments, the segmentation model  508  may be applied to the augmented training images to generate predicted segmentation masks for the augmented training images. The image augmentation may be applied to ground truth segmentation masks for training the segmentation model  508 . 
     In some embodiments, the segmentation model  508  may be trained using a two-pass model training technique, such as for training the segmentation model  508  to determine whether a first object is connected to a second object (e.g., whether a cable is properly/fully plugged into a connector). A first pass may be performed by the segmentation model  508  upon the image to estimate a region of a gap between the first object and the second object. A cropped image may be generated based upon the region. For example, the cropped image may depict a zoomed in view of the region encompassing the gap between the first object and the second object, while excluding other portions of the image (e.g., background, additional objects than the first object and the second object, etc.). The cropped image may have a higher resolution than a resolution of the image processed during the first pass. A second pass may be performed by the segmentation model  508  upon the cropped image to generate a prediction at a pixel level as to whether each pixels belongs to the region of the gap or not. If there is a threshold number of pixels within the region of the gap (e.g., there is a large gap between the cable and the connector), then the object may have a defect corresponding to the first object not being connected to the second object (e.g., a gap big enough to indicate that the cable is not fully plugged into the connector). If there is less than the threshold number of pixels within the region of the gap (e.g., there is little or no gap), then the object may be determined as not having the defect (e.g., the cable is fully plugged into the connector). 
     Once the segmentation model  508  has been trained to detect whether images comprise object regions of interest for objects with and without the defect, the segmentation model  508  and post processing may be used to determine whether input images depict an object with the defect or without the defect. 
       FIG.  6    illustrates an example of the defect detection system  506  utilizing the segmentation model  508  and post processing  616  to determine whether a screw  604  depicted within an image  602  has a defect of a stripped screw head or not. During operation  402  of method  400 , the image  602  depicting the screw  604  may be received by the defect detection system  506 . During operation  404  of method  400 , the defect detection system  506  may input the image  602  into the segmentation model  508  that has been trained to generation predictions on a per pixel basis as to whether each pixel of an input image depicts (is part of) an object region of interest (e.g., a screw head of the screw  604 ) of an object with a defect (e.g., a stripped screw head) or without the defect (e.g., a non-stripped and intact screw head). Accordingly, the defect detection system  506  uses the segmentation model  508  to process the image  602  depicting the screw  604 . 
     The segmentation model  508  may generate an output  612 , such as a segmentation mask or other output, based upon processing the image  602  depicting the screw  604 . For example, the segmentation model  508  may generate a prediction for each pixel within the image  602  as to whether each pixel depicts (is part of) an object region of interest  610  of the screw  604 , such as a screw head of the screw  604 . In this way, the output  612  from the segmentation model  508  identifies the object region of interest  610  of the screw  604 . For illustrative purposes, the object region of interest  610  is illustrated by black shading of the screw head. 
     In order to determine whether the screw head has the defect, post processing  616  is performed upon the output  612  from the segmentation model  508 . In particular, the post processing  616  calculates an object region area of the object region of interest  610 , during operation  406  of method  400 . In some embodiments, the object region area may correspond to a number of pixels within the object region of interest  610  (e.g., a number of pixels predicted by the segmentation model  508  as depicting the screw head). 
     During operation  408  of method  400 , the post processing  616  may identify a convex hull  614  (e.g., a smallest convex hull) encompassing the object region of interest  610 . For example, the convex hull  614  may correspond to a shape just large enough to fully enclose the object region of interest  610  (e.g., fully enclose the screw head) without cutting into the object region of interest  610 . The post processing  616  may calculate a convex hull area of the convex hull  614  encompassing the object region of interest  610 . In some embodiments, the post processing  616  may calculate the convex hull area of the convex hull  614  as a number of pixels within the convex hull  614 . 
     During operation  410  of method  400 , a ratio of the object region area to the convex hull area may be determined by the post processing  616 . The ratio may be indicative of a proportion or percentage of pixels that are both part of the object region of interest  610  (e.g., the screw head) and the convex hull  614  to pixels that are merely part of the convex hull  614 . The larger the percentage of pixels that are both part of the object region of interest  610  (e.g., the screw head) and the convex hull  614 , the more likely the screw  604  has a defect of a stripped screw head because the stripped screw head will be move convex that a non-stripped screw head (e.g., a cross shape of a non-stripped screw head will not be as convex as a stripped screw head). 
     In some embodiments, the post processing  616  takes the prediction from the segmentation model  508  (e.g., output  612  predicting which pixels are part to the object region of interest  610 ) as input, and outputs a convexity of the object region of interest  610  (e.g., a convexity of the screw head), which may be reflected as the ratio. The convexity may refer to a decimal between 0.0 (e.g., the prediction by the segmentation model  508  indicates that the object region of interest  610  such as the screw head is entirely non-convex) to 1.0 (e.g., the prediction by the segmentation model  508  indicates that the object region of interest  610  such as the screw head is entirely convex). The decimal (ratio) may be computed by dividing the object region area of the object region of interest  610  by the convex hull area of the convex hull  614 . In this way, the decimal represent convexity of the screw head, which may be determined as the ratio by the post processing  716 . 
     During operation  412  of method  400 , the ratio may be compared to a threshold to determine whether the screw  604  has the defect of a stripped screw head. For example, the threshold may correspond to a percentage of pixels that are both part of the object region of interest  610  (e.g., the screw head) and the convex hull  614 , such that if the ratio exceeds the threshold, then the screw  604  may be determined to have the defect of the stripped screw head. In this example, the ratio may be less than the threshold, which indicates that the percentage of pixels that are both part of the object region of interest  610  (e.g., the screw head) and the convex hull  614  is not enough to indicate that the screw  604  has the defect of the stripped screw head. In this way, the post processing  616  determines that the screw  604 , depicted within the image  602 , is not defective. 
       FIG.  7    illustrates an example of the defect detection system  506  utilizing the segmentation model  508  and post processing  716  to determine whether a screw  704  depicted within an image  702  has a defect of a stripped screw head or not. During operation  402  of method  400 , the image  702  depicting the screw  704  may be received by the defect detection system  506 . During operation  404  of method  400 , the defect detection system  506  may input the image  702  into the segmentation model  508  that has been trained to generation predictions on a per pixel basis as to whether each pixel of an input image depicts (is part of) an object region of interest (e.g., a screw head of the screw  704 ) of an object with a defect (e.g., a stripped screw head) or without the defect (e.g., a non-stripped and intact screw head). Accordingly, the defect detection system  506  uses the segmentation model  508  to process the image  702  depicting the screw  704 . 
     The segmentation model  508  may generate an output  712 , such as a segmentation mask or other output, based upon processing the image  702  depicting the screw  704 . For example, the segmentation model  508  may generate a prediction for each pixel within the image  702  as to whether each pixel depicts (is part of) an object region of interest  710  of the screw  704 , such as a screw head of the screw  704 . In this way, the output  712  from the segmentation model  508  identifies the object region of interest  710  of the screw  704 . For illustrative purposes, the object region of interest  710  is illustrated by black shading of the screw head. 
     In order to determine whether the screw head has the defect, post processing  716  is performed upon the output  712  from the segmentation model  508 . In particular, the post processing  716  calculates an object region area of the object region of interest  710 , during operation  406  of method  400 . In some embodiments, the object region area may correspond to a number of pixels within the object region of interest  710  (e.g., a number of pixels predicted by the segmentation model  508  as depicting the screw head). 
     During operation  408  of method  400 , the post processing  716  may identify a convex hull  714  (e.g., a smallest convex hull) encompassing the object region of interest  710 . For example, the convex hull  714  may correspond to a shape just large enough to fully enclose the object region of interest  710  (e.g., fully enclose the screw head) without cutting into the object region of interest  710 . The post processing  716  may calculate a convex hull area of the convex hull  714  encompassing the object region of interest  710 . In some embodiments, the post processing  716  may calculate the convex hull area of the convex hull  714  as a number of pixels within the convex hull  714 . 
     During operation  410  of method  400 , a ratio of the object region area to the convex hull area may be determined by the post processing  716 . The ratio may be indicative of a proportion or percentage of pixels that are both part of the object region of interest  710  (e.g., the screw head) and the convex hull  714  to pixels that are merely part of the convex hull  714 . The larger the percentage of pixels that are both part of the object region of interest  710  (e.g., the screw head) and the convex hull  714 , the more likely the screw  704  has a defect of a stripped screw head because the stripped screw head will be move convex that a non-stripped screw head (e.g., a cross shape of a non-stripped screw head will not be as convex as a stripped screw head). 
     In some embodiments, the post processing  716  takes the prediction from the segmentation model  508  (e.g., output  712  predicting which pixels are part to the object region of interest  710 ) as input, and outputs a convexity of the object region of interest  710  (e.g., a convexity of the screw head), which may be reflected as the ratio. The convexity may refer to a decimal (e.g., the ratio) between 0.0 (e.g., the prediction by the segmentation model  508  indicates that the object region of interest  710  such as the screw head is entirely non-convex) to 1.0 (e.g., the prediction by the segmentation model  508  indicates that the object region of interest  710  such as the screw head is entirely convex). The decimal may be computed by dividing the object region area of the object region of interest  710  by the convex hull area of the convex hull  714 . In this way, the decimal represent convexity of the screw head, which may be determined as the ratio by the post processing  716 . 
     During operation  412  of method  400 , the ratio may be compared to a threshold to determine whether the screw  704  has the defect of a stripped screw head. For example, the threshold may correspond to a percentage of pixels that are both part of the object region of interest  710  (e.g., the screw head) and the convex hull  714 , such that if the ratio exceeds the threshold, then the screw  704  may be determined to have the defect of the stripped screw head. In this example, the ratio may exceed the threshold, which indicates that the percentage of pixels that are both part of the object region of interest  710  (e.g., the screw head) and the convex hull  714  is enough to indicate that the screw  704  has the defect of the stripped screw head. In this way, the post processing  716  determines that the screw  704 , depicted within the image  702 , is defective. 
     In response to determining that the screw  704  is defective, the defect detection system  506  may implement various remedial actions to address the defect. For example, a description of the defected may be generated and displayed on a computing device. In another example, instructions for how to address/fix the defect may be generated and displayed on the computing device (e.g., instructions for how to remove and replace the screw  704  with a stripped screw head). In another example, the screw  704  and/or object comprising the screw may be marked as defective and replacement of the screw  704  and/or object may be facilitated. In another example, a machine (e.g., a factory machine such as a robotic arm) is controlled to discard the screw  704  and/or object, replace the screw  704  and/or object with a replacement the screw  704  and/or object, or correct the defect, such as where commands are sent to the machine to remove and replace the defective screw. 
     Various other types of defects may be identified for different types of objects. 
       FIG.  8    illustrates an example of the defect detection system  506  utilizing the segmentation model  508  to evaluate an image  802  depicting a cable  806  and a connector  804  to determine whether the cable  806  has a defect of not being fully connected (plugged into) the connector  804 . The segmentation model  508  may perform a first pass to estimate a region  811  of a gap between the cable  806  and the connector  804 . A cropped image  809  may be generated based upon the region  811  of the gap. The cropped image  809  may correspond to a zoomed in view of the region  811 . The segmentation model  508  may perform a second pass upon the cropped image  809  to generate a prediction at a pixel level as to whether pixels belong to the region  811  of the gap. The more pixels that below to the region  811  of the gap compared to pixels that do not, the more likely the cable  806  and the connector  804  have a defect  812  of not being fully connected. 
     In some embodiments, an image may be evaluated to determine that an object depicted in the image is a first type of object (e.g., a screw, a bolt, or any other object that will be more convex if defective) and that a first type of defect is to be detected (e.g., a stripped screw head or stripped bolt head). The first type of object and/or the first type of defect may correspond to where the more similar an object region area is to a convex hull area (e.g., the ratio exceeding the threshold, and thus having high convexity), the more likely the object has the defect. Accordingly, the object may be identified as having the defect within the object region of interest based upon the ratio exceeding the threshold. An example of detecting this type of defect has been described in relation to  FIG.  7   . 
     In contrast, an image may be evaluated to determine that an object depicted in the image is a second type of object (e.g., a jar, a bucket, a container, a housing, a laptop, a phone, a watch, a toy, or any other object that may be composed of a material or part that could have a defect of being dented, chipped, missing, etc.) and that a second type of defect is to be detected (e.g., a dent, a missing part, missing material, a chip, etc.). The second type of object and/or the second type of defect may correspond to where the more dissimilar an object region area is to a convex hull area (e.g., the ratio not exceeding the threshold, and thus having low convexity), the more likely the object has the defect. Accordingly, the object may be identified as having the defect within the object region of interest based upon the ratio not exceeding the threshold (e.g., a portion of the object has been chipped off, and thus has lower convexity). An example of detecting this type of defect is illustrated by  FIG.  9   . 
       FIG.  9    illustrates an example of the defect detection system  506  utilizing the segmentation model  508  to evaluate an image  902  depicting an object  904  that is the second type of object where the more dissimilar an object region area is to a convex hull area (e.g., the ratio not exceeding the threshold, and thus having low convexity), the more likely the object  904  has the defect. In this example, the defect detection system  506  may determine that the object  904  is defective  912  based upon the object  904  comprising a chip of missing material at a top left corner of the object  904 . 
     In some embodiments, alignment of a first object with respect to a second object may be determined and used to detect a defect (e.g., whether a label has been installed in a correct location). The segmentation model  508  may be used to detect an outline of the first object, such as an outline of the label. A second segmentation model may be used to detect an intersection outline of an intersection between where the first object intersects the second object (e.g., where the label has been installed on a bottom side of a laptop). In this way, two different models are used on two different objects. The outputs of the segmentation model  508  (e.g., the outline of the first object such as the label) and the second segmentation model (e.g., the intersection outline of the intersection between where the first object intersects the second object such as where the label is affixed to the bottom side of the laptop) may be used to determine whether there is a defect corresponding to misalignment of the first object with respect to the second object (e.g., the label does not align correctly with the bottom of the laptop). 
       FIG.  10    is an illustration of a scenario  1000  involving an example non-transitory machine readable medium  1002 . The non-transitory machine readable medium  1002  may comprise processor-executable instructions  1012  that when executed by a processor  1016  cause performance (e.g., by the processor  1016 ) of at least some of the provisions herein. The non-transitory machine readable medium  1002  may comprise a memory semiconductor (e.g., a semiconductor utilizing static random access memory (SRAM), dynamic random access memory (DRAM), and/or synchronous dynamic random access memory (SDRAM) technologies), a platter of a hard disk drive, a flash memory device, or a magnetic or optical disc (such as a compact disk (CD), a digital versatile disk (DVD), or floppy disk). The example non-transitory machine readable medium  1002  stores computer-readable data  1004  that, when subjected to reading  1006  by a reader  1010  of a device  1008  (e.g., a read head of a hard disk drive, or a read operation invoked on a solid-state storage device), express the processor-executable instructions  1012 . In some embodiments, the processor-executable instructions  1012 , when executed cause performance of operations, such as at least some of the example method  400  of  FIG.  4   , for example. In some embodiments, the processor-executable instructions  1012  are configured to cause implementation of a system, such as at least some of the example systems  500 ,  600 ,  700 ,  800 , and  900  of  FIGS.  5 - 9   , for example. 
     3. Usage of Terms 
     As used in this application, “component,” “module,” “system”, “interface”, and/or the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. 
     Unless specified otherwise, “first,” “second,” and/or the like are not intended to imply a temporal aspect, a spatial aspect, an ordering, etc. Rather, such terms are merely used as identifiers, names, etc. for features, elements, items, etc. For example, a first object and a second object generally correspond to object A and object B or two different or two identical objects or the same object. 
     Moreover, “example” is used herein to mean serving as an example, instance, illustration, etc., and not necessarily as advantageous. As used herein, “or” is intended to mean an inclusive “or” rather than an exclusive “or”. In addition, “a” and “an” as used in this application are generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Also, at least one of A and B and/or the like generally means A or B or both A and B. Furthermore, to the extent that “includes”, “having”, “has”, “with”, and/or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising”. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing at least some of the claims. 
     Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
     Various operations of embodiments are provided herein. In an embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. Also, it will be understood that not all operations are necessary in some embodiments. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.