Patent Publication Number: US-2020296265-A1

Title: Processing system

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to a processing system including an imaging system. 
     Description of the Related Art 
     An inspection technique using a terahertz wave is known. Japanese Patent Laid-Open No. 2004-286716 discloses a method of inspecting a prohibited drug such as a narcotic drug enclosed in a sealed letter. 
     When processing an image acquired by a terahertz wave for inspection, it may be impossible to obtain a sufficient information amount because of the positional relationship between an inspection target and a detection unit or the movement of the inspection target. In addition, when inspecting a dressed person, scattering may occur due to clothes, or propagation of terahertz waves may be impeded by the environment. Therefore, sufficient inspections may be impossible. 
     SUMMARY OF THE INVENTION 
     The present invention has been made in consideration of the above situation, and provides a processing system capable of more advantageously executing an inspection using a terahertz wave. 
     According to the first aspect of the present invention, there is provided a processing system comprising a first imaging system configured to capture a first image based on a terahertz wave from an inspection target; a second imaging system configured to capture a second image of the inspection target based on an electromagnetic wave of a wavelength different from the terahertz wave, and a processor configured to process the first image and the second image, wherein the processor detects an inspection region based on the second image and processes information of a region of the first image corresponding to the inspection region. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a conceptual view of a processing system according to the first embodiment; 
         FIG. 2  is a flowchart of processing according to the first embodiment; 
         FIG. 3  is a conceptual view of a processing system according to the second embodiment; 
         FIG. 4  is a flowchart of processing according to the second embodiment; 
         FIG. 5  is a conceptual view of a processing system according to the third embodiment; 
         FIG. 6  is a flowchart of processing according to the third embodiment; 
         FIG. 7  is a conceptual view of a processing system according to the fourth embodiment; 
         FIG. 8  is a conceptual view of a processing system according to the fifth embodiment; 
         FIG. 9  is a view showing an arrangement example of the processing system; 
         FIG. 10  is a view showing an arrangement example of the processing system; 
         FIG. 11A  is a view showing an example of a model for machine learning; 
         FIG. 11B  is a view showing an example of an inspection using a learned model; 
         FIG. 12A  is a view showing an example of a model for machine learning; 
       and 
         FIG. 12B  is a view showing an example of control using a learned model. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. It should be noted that the following embodiments are not intended to limit the scope of the appended claims. A plurality of features are described in the embodiments. Not all the plurality of features are necessarily essential to the present invention, and the plurality of features may arbitrarily be combined. Furthermore, the same reference numerals denote the same parts throughout the accompanying drawings, and a repetitive description thereof will be omitted. In the present invention, terahertz waves include electromagnetic waves within the frequency range of 30 GHz to 30 THz. The concept of electromagnetic waves can include visible light, infrared light, and a radio wave such as a millimeter wave. 
     First Embodiment 
     The outline of a processing system  401  according to the first embodiment will be described with reference to  FIG. 1 . The processing system  401  includes a first imaging system including a first illumination source  404  and a first camera  402 , a second imaging system including a second camera  405 , and a processor including a pre-processing unit  406  and a post-processing unit  407 . 
     The first camera  402  of the first imaging system acquires a first image based on a terahertz wave  403  of a first wavelength radiated from the first illumination source  404 . An inspection target  410  is irradiated with the terahertz wave  403  radiated from the first illumination source  404 . If the inspection target  410  is a dressed person, the terahertz wave  403  passes through the fibers of clothes and is reflected by a metal or ceramic held by the inspection target  410 . A specific substance, for example, RDX (trimethylenetrinitroamine) that is an explosive is known to absorb a terahertz wave near 0.8 THz, and therefore, the reflected wave decreases. The first camera  402  acquires the first image based on the reflected wave. 
     The second camera  405  of the second imaging system acquires a second image from an electromagnetic wave of a wavelength different from that of the terahertz wave irradiated from the first illumination source  404 . As the electromagnetic wave of a different wavelength, visible light, infrared light, or a millimeter wave can be used. When using infrared light, an illumination source (not shown) different from the first illumination source  404  may be prepared. The second image acquired by the second camera  405  is processed by the pre-processing unit  406 . The pre-processing unit  406  performs processing of detecting an inspection region from the second image. 
     If the second image is acquired by visible light, and the inspection target  410  is a person, detection of the inspection region may be performed by detecting a specific part of clothes as the inspection region. The inspection region may be specified by creating a model by machine learning and classifying a region of the captured second image by the model. Alternatively, the region may be specified based on the information of the shape of an object stored in a database  409 . If the second image is acquired by a millimeter wave, a portion where the intensity distribution in the image is higher than a threshold or a portion where the intensity difference is large may be detected as the inspection region. If infrared light is used to acquire the second image, a portion with little radiation of infrared light caused by water or a specific portion of clothes in an image detected by night vision may be detected as the inspection region. Even in a dark place or a place with a poor view due to the weather, the inspection region can be detected using the infrared light or a millimeter wave. When detecting the inspection region from an image of a dressed person, an unnaturally swelling portion of clothes, the chest portion of the person, or a pocket portion of clothes may be detected as the inspection region. 
     The inspection of the inspection target  410  by the processor will be described based on  FIG. 2 . The pre-processing unit  406  detects the inspection region from the second image acquired by the second camera  405  (step S 421 ) by the above-described method (steps S 422  and S 423 ). The post-processing unit  407  performs processing of image data for the information of the region of the first image corresponding to the inspection region detected from the second image (step S 425 ). The first image is an image acquired by the first camera  402  using a terahertz wave (step S 424 ), and is an image obtained by seeing through clothes or the like. If a metal or ceramic object exists under the clothes, an image can be obtained from the reflected wave. Hence, the shape of the object can be detected by processing the first image. After the inspection region is detected from the second image, the region in the first image corresponding to the inspection region is selected by comparing the first image and the second image. Subsequent image processing for the first image is performed for the region corresponding to the inspection region detected from the second image. 
     When the region corresponding to the inspection region is selected from the first image, and image processing is performed, the processing can be performed while reducing unnecessary information. For this reason, the processing load can be reduced as compared to processing of the entire image data, and the speed can be increased. Hence, even if the inspection target  410  is moving, features can be detected from the first image a plurality of times in a short moving distance during a short time. A determination unit  408  estimates the object under the clothes based on the plurality of detected features (step S 426 ). The plurality of features may be features of a part of the object. The determination unit  408  may classify the shape of the object detected from the first image based on the data in the database  409 . The classification may be done using a model created by machine learning. It is considered that the information of the shape obtained from the image may be the information of a part of the object because of the movement of the inspection target  410  or the positional relationship between the inspection target and the camera. Even in this case, the estimation accuracy can be improved by classifying the features based on the information of the plurality of features, accumulating a plurality of results, and performing determination based on the accumulated classification results (step S 427 ). 
     When the processing system is used in a security monitoring system, the risk of the object detected from the inspection region is determined based on the accumulation of the classification results for the inspection target  410  (step S 428 ). As for the determination, determination based on the accumulation result of classifications may be performed based on a model by machine learning. If it is determined that the inspection target  410  holds a dangerous substance, it is possible to notify the outside that the inspection target  410  holds a dangerous substance. When the inspection target  410  passes through a gate in which the processing system is arranged, the processing system may notify the outside of a warning. When the inspection target  410  puts in a ticket and passes through a ticket gate, the processing system may link the ticket with the inspection target  410  and notify that the inspection target  410  is a monitoring target. If the second image is obtained using visible light, the inspection target  410  can be displayed such that it can easily be seen by displaying the second image and the first image on a monitor in a superimposed manner. When the determination is suspended, the inspection is repeated until the end condition is satisfied. The end condition may be the number of repetitions of the inspection (S 429 ). 
     A method of specifying, from the second image acquired by the second camera  405 , the inspection region using a model (artificial intelligence) created by machine learning will be described next in detail. 
       FIG. 11A  is a view schematically showing a model for machine learning, that is, a learning model. In this example, a neural network including an input layer  481 , an output layer  483 , and at least one intermediate layer  482  is used as a learning model. Image data is input to the input layer  481 . In addition, the output layer  483  outputs a feature amount indicating a partial region of the input image. 
     As a learning method of the learning model, supervisory data with a correct answer label can be used. That is, using a data group including a set of input image data and a label representing an inspection target region in the image data, the learning model is learned by a means such as backpropagation. The target region includes a person, a bag, a container, and the like, but is not limited to these. Learning by deep learning may be performed using a CNN (Convolutional Neural Network) as a model. 
       FIG. 11B  is a schematic view showing a specifying method of an inspection region using a learned model. As an input, a visible light image  484  is input. A learned model  485  outputs a feature amount representing an inspection target region. As the form of output, for example, a target region in an image  486  is surrounded by a line, as shown in  FIG. 11B . Alternatively, coordinate information for image processing may be output. 
     When determination using machine learning or artificial intelligence is performed in this way, accurate determination can be performed. 
     In addition, when detecting a specific target object from the first image, similarly, a model (artificial intelligence) created by machine learning may be used. In this case, as supervisory data for learning, an image of a terahertz wave having the same wavelength as the terahertz wave used by the first camera  402  for capturing is used. 
     Second Embodiment 
     In this embodiment, a second imaging system is provided with a second illumination source  411  that radiates a terahertz wave. This embodiment will be described with reference to  FIG. 3 . The second illumination source  411  is an illumination source that generates a terahertz wave of a second wavelength different from a first illumination source  404 . As described in the first embodiment, there is known a specific substance that absorbs a terahertz wave of a specific wavelength. Hence, a terahertz wave of a first wavelength (about 0.8 THz for RDX that is an explosive) that is a wavelength the specific substance readily absorbs is radiated from the first illumination source  404  to an inspection target  410 . If the inspection target  410  holds a substance with a characteristic to easily absorb the terahertz wave of the first wavelength, reflection in the portion where the substance is held become small. On the other hand, when a wavelength (about 0.5 THz when the first wavelength is 0.8 THz) that is little absorbed by the specific substance is selected as the terahertz wave of the second wavelength generated by the second illumination source  411 , the specific substance reflects the terahertz wave of the second wavelength. The substance can be specified using the difference between reflected waves from the specific substance for the same inspection region. 
     Processing according to this embodiment will be described based on  FIG. 4 . A pre-processing unit  406  detects, as the inspection region, a high reflection region in the second image acquired by the terahertz wave of the second wavelength (steps S 431  and S 432 ). A post-processing unit  407  acquires a first image (step S 434 ) captured by a first camera  402  based on a terahertz wave of a first wavelength, and starts processing image data for a region of the first image corresponding to the inspection region detected from the second image. The post-processing unit  407  can calculate the difference between the information of the inspection region in the second image and the information of the region of the first image corresponding to the inspection region (step S 435 ). Data of a portion where reflection and absorption in the second image are almost equal to those in the first image is almost canceled by calculating the difference between the two pieces of information. However, data of a portion where reflection and absorption are different between the first wavelength and the second wavelength is not canceled even by calculating the difference between the two images. In this way, the spectrum analysis of the substance in the inspection region can be performed using the difference in the rate of terahertz wave absorption by the substance. The type of the substance can be estimated using the spectrum analysis. In addition, since scattering or reflection by clothes is canceled, an unnecessary signal from the clothes can be reduced from the obtained image information, and the signal-to-noise ratio of the image can be improved. 
     If the inspection target person holds a substance that readily absorbs the first wavelength, the substance detected in the inspection region can be classified based on the difference in the absorption rate between the first wavelength and the second wavelength (step S 436 ). As for the classification, when the relationship between a specific substance and a wavelength is held in a database  409 , a determination unit  408  can perform the classification based on the database  409 . The determination unit  408  may perform the classification using a model created by machine learning. With the above-described method, it is possible to estimate that the inspection target  410  holds the substance that absorbs the specific wavelength. It is known that dangerous substances exist among the substances that absorb a terahertz wave of a specific wavelength. The existence of a dangerous substance can be estimated by spectrum analysis. The detection accuracy can be raised by accumulating a plurality of spectrum analysis results (step S 437 ). It is thus determined that the inspection target  410  may hold a dangerous substance (step S 438 ). As for the determination, determination based on the accumulation result of classifications may be performed based on a model by machine learning. If it is determined that a dangerous substance is held, the processing system notifies the outside that the inspection target  410  holds a dangerous substance. When the inspection target  410  passes through a gate in which the processing system is arranged, the processing system may notify the outside of a warning. When the person of the inspection target  410  puts in a ticket and passes through a ticket gate, the processing system may link the ticket with the inspection target  410  and notify the outside of the person as a monitoring target. As for the wavelength of the terahertz wave irradiated from the second illumination source  411 , a plurality of illumination sources capable of irradiating terahertz waves of a plurality of, that is, three or more wavelengths may be combined in accordance with the absorption spectrum of a substance to be detected. When the determination is suspended, the inspection is repeated until the end condition is satisfied. The end condition may be the number of repetitions of the inspection (S 439 ). 
     Third Embodiment 
     In this embodiment, based on detection of a specific region in a second image captured by a second imaging system a control unit  412  is controlled to control a first illumination source  404  and a first camera  402  in a first imaging system. This embodiment will be described with reference to  FIGS. 5 and 6 . 
     A second camera  405  of the second imaging system acquires a second image from an electromagnetic wave of a wavelength different from a terahertz wave radiated from the first illumination source  404 . As the electromagnetic wave of a different wavelength, visible light, infrared light, or a millimeter wave can be used. The second image acquired by the second camera  405  is processed by a pre-processing unit  406 . The pre-processing unit  406  detects an inspection region from the second image (steps S 452  and S 453 ). Detection of the inspection region is performed as described in the first embodiment. 
     Conditions at the time of capturing by the first camera are controlled in accordance with the position and range of the inspection region detected from the second image and the state of the inspection region. The conditions include control of the posture of the first camera, control of a gain for an acquired image, and control of a capturing range for zooming or trimming and an angle of view (step S 454 ). The output level (output power) and the wavelength of the terahertz wave irradiated from the first illumination source  404  may be changed in accordance with the strength of a reflected signal from the inspection region or a target object in the inspection region. By this control, the inspection accuracy can be raised. The first imaging system controlled by the control unit  412  acquires a first image based on the terahertz wave of a first wavelength (step S 455 ). A post-processing unit  407  performs processing of the inspection region based on the acquired first image (step S 456 ). After that, a determination unit  408  performs determination and classification of an object (steps S 457 , S 458 , and S 459 ). When the processing system is a security monitoring system, a risk is determined based on the accumulation of classification results. If it is determined that an inspection target  410  holds a dangerous substance, the processing system notifies the outside that the inspection target  410  holds a dangerous substance. When the inspection target  410  passes through a gate in which the processing system is arranged, the processing system may notify the outside of a warning. When the inspection target  410  puts in a ticket and passes through a ticket gate, the processing system may link the ticket with the inspection target  410  and set the inspection target  410  to a monitoring target. When the determination is suspended, the inspection is repeated until the end condition is satisfied. The end condition may be the number of repetitions of the inspection (S 460 ). 
     Capturing by the first camera  402  may be controlled by a model (artificial intelligence) created by machine learning from the second image acquired by the second camera  405 . The method will be described in detail. 
       FIG. 12A  is a view schematically showing a model for machine learning, that is, a learning model. In this example, a neural network including an input layer  481 , an output layer  483 , and at least one intermediate layer  482  is used as a learning model. Image data is input to the input layer  481 . In addition, the output layer  483  can output the classification result of the object of the input image. 
     As a learning method of the learning model, supervisory data with a correct answer label can be used. That is, using a data group including a set of input image data and a label representing an inspection target region in the image data, the learning model is learned by a means such as backpropagation. Learning by deep learning may be performed using a CNN (Convolutional Neural Network) as a model. 
     The data for classification of the object is selected in accordance with the purpose of the control. To control zooming, supervisory data with a label representing that the object is small or large, or has an appropriate size is used. To control the gain, supervisory data with a label representing that exposure of the object is underexposure, appropriate exposure, or overexposure is used. To control to switch the wavelength used by the first camera  402 , supervisory data in which an input image is associated with an appropriate wavelength band is used. Alternatively, to control the output of the first illumination source  404 , supervisory data in which classification is done in accordance with the transmittance of the terahertz wave output from the first illumination source  404  is used. These supervisory data are merely examples, and are not limited to these. Learning may be performed by deep learning without supervisory data. In this case, learning can be performed by a means for evaluating a result of control in accordance with an output to an input. 
       FIG. 12B  is a schematic view showing a method of controlling the first camera  402  using a learned model. As an input, a visible light image  487  is input. A learned model  488  can output information representing the presence/absence of an object of low sensitivity for a terahertz wave in a wavelength range detected by the first camera  402 . In accordance with the result, control to increase the output of the first illumination source  404  is performed, thereby obtaining an image  489 . 
     When determination using machine learning or artificial intelligence is performed in this way, the accuracy of detection of the target object using the first camera  402  can be made higher. 
     In addition, when detecting a specific target object from the first image, similarly, a model (artificial intelligence) created by machine learning may be used. In this case, as supervisory data for learning, an image of a terahertz wave having the same wavelength as the terahertz wave used by the first camera  402  for capturing is used. 
     Fourth Embodiment 
     In this embodiment, an environment monitoring unit  413  configured to monitor a humidity around a processing unit is provided. This embodiment will be described with reference to  FIG. 7 . A terahertz wave is readily absorbed by water vapor. A terahertz wave of a longer wavelength is hardly affected by water vapor. Hence, the environment monitoring unit  413  is provided to measure the humidity, and the imaging system is controlled so as to be hardly affected by the peripheral environment. 
     More specifically, if the environment monitoring unit  413  detects that the humidity has become high, the wavelength of a terahertz wave  403  radiated from a first illumination source  404  is switched to a wavelength longer than the wavelength currently under use. In accordance with the humidity, the wavelength may be switched to a wavelength (a region that exists near a wavelength of 1.2 mm or 0.75 mm, where attenuation of atmosphere is specifically small) hardly affected by water vapor. When the wavelength of the terahertz wave becomes long, the resolution of an image captured by the camera lowers. However, it is possible to reduce the influence of water vapor and continue inspection. 
     Fifth Embodiment 
     In this embodiment, capturing is performed using terahertz waves of different wavelengths. A second image is acquired using a terahertz wave of a second wavelength longer than the wavelength in capturing a first image, and an inspection region is detected from the second image. The inspection region may be detected as a region including an object of a predetermined shape using a model created by machine learning, or a region where the spectrum of a reflected wave of a predetermined wavelength changes may be detected as the inspection region. 
     This embodiment will be described with reference to  FIG. 8 . Based on the inspection region detected from the second image, image data of a region of the first image corresponding to the inspection region is processed. The first image captured by a terahertz wave of a first wavelength generated from a first illumination source  404  is capturing  1 , and the second image captured by a terahertz wave of a second wavelength generated from an illumination source  411  is capturing  2 . Since the image of capturing  1  is acquired using a terahertz wave of a wavelength shorter than that in capturing  2 , the resolution is high, and the information amount is large. Hence, the shape of each object held by an inspection target  410  is clear in the image acquired by the terahertz wave. However, since the terahertz wave of a short wavelength is used, the depth of field is shallow, and the capturing is sensitive to a change in the posture of the inspection target  410 . More specifically, depending on the posture of the inspection target  410 , a partial shape is acquired as the shape of the object held by the inspection target  410 . On the other hand, in the image obtained by capturing  2 , since the wavelength of the terahertz wave is long, the resolution is low, and the shape of each object is not clear as compared to capturing  1 . However, since the terahertz wave of a long wavelength is used, the depth of field is deep, and the capturing is insensitive to a change in the posture of the inspection target  410 . More specifically, independently of the posture of the inspection target  410 , the whole shape of the object held by the inspection target  410  is acquired. When capturing  2  of a low resolution is processed to specify the position of an object held by the inspection target  410 , and the data of capturing  1  is processed based on the detected inspection region, the processing load can be reduced, and the processing can be performed at a higher speed. Hence, even if the inspection target  410  is moving, features of the inspection target  410  can be detected a plurality of times in a short moving distance during a short time, and the object under clothes can be estimated based on the detected features. 
     In addition, when the difference between capturing  1  and capturing  2  performed using terahertz waves of the two different wavelengths is calculated, reflection by clothes is canceled, and noise can be reduced from the obtained image information. More specifically, since scattering is the main component of reflection from whole clothes, the intensity difference is small, and the capturing is insensitive to a change in the posture of the inspection target  410  (random noise is added to the acquired image as a whole). For this reason, when the differential image between capturing  1  and capturing  2  is calculated, the signal of clothes is canceled. In addition, when the difference is calculated, an image based on the difference in the terahertz wave absorption rate of the substance through which the terahertz wave passes can be obtained. Hence, the shape of an object containing a substance other than a metal or ceramic as a component can be detected from the difference between the first image and the second image. 
     The object in the inspection region is estimated by a determination unit  408  by classifying the shape of the object detected from capturing  1 . If the inspection target  410  moves, the shape of the object obtained from the image is often partial. Hence, the determination accuracy can be improved by accumulating a plurality of classification results and performing determination based on the accumulated classification results. In a case of a security monitoring system, a risk is determined based on the accumulation of classification results. If it is determined that the inspection target  410  holds a dangerous substance, the processing system notifies that the inspection target  410  holds a dangerous substance. When the inspection target  410  passes through a gate in which the processing system is arranged, the processing system may notify the outside of a warning. When the inspection target  410  puts in a ticket and passes through a ticket gate, the processing system may link the ticket with the inspection target  410  and set the inspection target  410  to a monitoring target. 
     Sixth Embodiment 
     An application example of the processing system will be described with reference to  FIGS. 9 and 10 .  FIG. 9  shows an example in which a first illumination source  404  for a terahertz wave of a first wavelength and a second illumination source  411  of a second wavelength different from the first wavelength are arranged on one side of a doorway  414  of a vehicle or the like. A first camera  402  configured to perform capturing based on the terahertz wave of the first wavelength, a second camera  405 - 1  configured to perform capturing based on one of visible light, infrared light, and a millimeter wave, and a second camera  405 - 2  configured to perform capturing based on the terahertz wave of the second wavelength are arranged on the other side of the doorway  414 . When the cameras and the illumination sources are combined, the processes concerning inspection described in the first to fifth embodiments can be performed in combination. An inspection target  410  can be tracked by the second camera  405 - 1 , and the posture and the angle of view of the first camera  402  can be controlled. When the wavelength of the terahertz wave used for capturing by the second camera  405 - 2  configured to perform capturing based on a terahertz wave is set in accordance with the absorption rate of a substance, spectrum analysis can be performed. In addition, when the second cameras  405 - 1  and  405 - 2  are used to detect an inspection region, the processing load for a first image captured by the first camera  402  can be reduced. Furthermore, the shape of an object containing a substance other than a metal or ceramic as a component can be detected using the difference in the absorption rate of the substance for the wavelength of the terahertz wave. In this embodiment, as the second cameras  405 , a camera for visible light, infrared light, or a millimeter wave and a camera for a terahertz wave of a second wavelength are used. However, only one of the camera for visible light, infrared light, or a millimeter wave and the camera for a terahertz wave of a second wavelength may be used as the second camera. The illumination sources and the cameras can unnoticeably be buried in a wall surface, a ceiling, or a floor surface. The illumination sources and the cameras may be arranged on both of the left and right sides of the doorway  414 . When the illumination sources and the cameras are provided near the doorway  414 , situations in which a plurality of inspection targets  410  overlap can be reduced, and the inspection accuracy can be improved. 
     An example in which the processing system is arranged near a ticket gate machine  415  installed at a ticket gate of a station will be described with reference to  FIG. 10 . The first illumination source  404  for a terahertz wave of a first wavelength and the second illumination source  411  of a second wavelength different from the first wavelength are arranged on one side of the ticket gate machine  415 . The first camera  402  configured to perform capturing based on the terahertz wave of the first wavelength, the second camera  405 - 1  configured to perform capturing based on one of visible light, infrared light, and a millimeter wave, and the second camera  405 - 2  configured to perform capturing based on the terahertz wave of the second wavelength are arranged on the other side of the ticket gate machine  415 . When the processing system is arranged near the ticket gate machine  415 , situations in which a plurality of inspection targets  410  overlap can be reduced, and the inspection accuracy can be improved. 
     The operation of the processing system may be started in accordance with detection of a motion of the inspection target  410  by a sensor provided separately from the processing system opening/closing of a door of a vehicle, putting of a ticket into the ticket gate machine  415 , or the like. A plurality of first cameras and second cameras may be provided. By using a plurality of cameras, the detection accuracy can be improved, the number of inspection targets can be increased, and the inspection region can be expanded. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Applications No. 2019-047789 filed Mar. 14, 2019 and No. 2020-032195 filed Feb. 27, 2020, which are hereby incorporated by reference herein in their entirety.