Escalator comb tooth anomaly detection method, device and equipment and storage medium

Abstract

The application discloses an escalator comb tooth abnormality detection method, device and equipment and a storage medium. The application determines whether a specific area of a comb plate is abnormal by comprehensively analyzing the analysis result of the specific area of the comb plate in a plurality of angle shot static images, and further improves the abnormality determination accuracy. Meanwhile, the original mechanical structure of the escalator does not need to be changed, and the cost is reduced.

Description

Technical Field

[0001] This invention relates to escalator safety monitoring technology, and more particularly to a method, device, equipment, and storage medium for detecting abnormal escalator comb teeth. Background Technology

[0002] Escalators are a common mode of transportation in public places. Pedestrians stand on the automatically moving steps at one end of the escalator and are automatically carried to the other end. Comb plates are located at both ends of the escalator entrance / exit, facilitating passenger transition and engaging with the steps, treads, or moving walkway belts. During operation, the meshing teeth of the steps and the comb teeth of the comb plate mesh well, allowing the steps to smoothly enter the return path and preventing external objects from being brought into the passenger conveyor. As the end of the escalator, the comb plate plays a crucial role in passenger safety. If the comb teeth break or are missing, it could cause injury to a passenger's feet.

[0003] In existing technologies, most methods use mechanical devices to detect whether the comb teeth are moving abnormally in order to determine whether the comb teeth are normal. However, this method requires modification of the internal structure of the escalator, which is costly and has low reliability. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for detecting abnormal comb teeth in escalators, thereby improving the accuracy of comb tooth abnormality determination without altering the original mechanical structure of the escalator and reducing costs.

[0005] In a first aspect, the present invention provides a method for detecting abnormal comb teeth in escalators, comprising:

[0006] Acquire a first still image captured by a first camera, a second still image captured by a second camera, and a third still image captured by a third camera. The first camera is positioned at the first end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The second camera is positioned at the second end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The third camera is positioned vertically above the comb plate and captures images of the comb plate along a direction perpendicular to the comb plate.

[0007] Based on the first static image, analyze whether there are any abnormalities in the first region, second region, and third region of the comb plate; based on the second static image, analyze whether there are any abnormalities in the first region, second region, and third region of the comb plate; based on the third static image, analyze whether there are any abnormalities in the first region, second region, and third region of the comb plate. The comb plate is divided into a first region, a second region, and a third region along its length, and the first region and the third region are located on both sides of the second region.

[0008] By combining the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair, it is determined whether there is an anomaly in the first region of the comb plate;

[0009] By combining the analysis results of the second region of the first static image pair, the analysis results of the second region of the second static image pair, and the analysis results of the second region of the third static image pair, it is determined whether there is an anomaly in the second region of the comb plate;

[0010] By combining the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair, it is determined whether there is an anomaly in the third region of the comb plate.

[0011] Optionally, analyzing whether there are any anomalies in the first, second, and third regions of the comb plate based on the first static image, analyzing whether there are any anomalies in the first, second, and third regions of the comb plate based on the second static image, and analyzing whether there are any anomalies in the first, second, and third regions of the comb plate based on the third static image, includes:

[0012] The first static image is subjected to image morphology processing to extract comb tooth features from the first static image.

[0013] The comb tooth features extracted from the first static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first, second, and third regions of the comb plate.

[0014] The second static image is subjected to image morphology processing to extract comb tooth features from the second static image;

[0015] The comb tooth features extracted from the second static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first, second, and third regions of the comb plate.

[0016] The third static image is subjected to image morphology processing to extract comb tooth features from the third static image;

[0017] The comb tooth features extracted from the third static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first, second, and third regions of the comb plate.

[0018] Optionally, by combining the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair, it is determined whether there is an anomaly in the first region of the comb plate, including:

[0019] Based on the analysis results of the first region of the first static image, the analysis results of the first region of the second static image, and the analysis results of the first region of the third static image, a preset first confidence level, a second confidence level, and a third confidence level are assigned to the first region, respectively.

[0020] Calculate the sum of the first confidence level, the second confidence level, and the third confidence level to obtain the first total confidence level that the first region has an anomaly;

[0021] Determine whether the first overall credibility is greater than a preset value;

[0022] If the first total confidence level is greater than a preset value, then it is determined that there is an anomaly in the first region;

[0023] If the first total confidence level is not greater than a preset value, then it is determined that there is no anomaly in the first region.

[0024] Optionally, by combining the analysis results of the second region of the first static image pair, the analysis results of the second region of the second static image pair, and the analysis results of the second region of the third static image pair, it is determined whether there is an anomaly in the second region of the comb plate, including:

[0025] Based on the analysis results of the second region of the first static image, the analysis results of the second region of the second static image, and the analysis results of the second region of the third static image, a preset fourth confidence level, a fifth confidence level, and a sixth confidence level are assigned to the second region, respectively.

[0026] The sum of the fourth confidence level, the fifth confidence level, and the sixth confidence level is calculated to obtain the second total confidence level that the second region has an anomaly;

[0027] Determine whether the second overall credibility is greater than a preset value;

[0028] If the second total confidence level is greater than the preset value, then the second region is determined to be abnormal;

[0029] If the second total confidence level is not greater than the preset value, then it is determined that there is no anomaly in the second region.

[0030] Optionally, by combining the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair, it is determined whether there is an anomaly in the third region of the comb plate, including:

[0031] Based on the analysis results of the third region of the first static image, the analysis results of the third region of the second static image, and the analysis results of the third region of the third static image, preset seventh confidence level, eighth confidence level, and ninth confidence level are assigned to the third region respectively.

[0032] Calculate the sum of the seventh confidence level, the eighth confidence level, and the ninth confidence level to obtain the third total confidence level that the third region has an anomaly;

[0033] Determine whether the third overall credibility is greater than a preset value;

[0034] If the third overall confidence level is greater than a preset value, then the third region is determined to be abnormal;

[0035] If the third overall confidence level is not greater than a preset value, then it is determined that there is no anomaly in the third region.

[0036] Optionally, before acquiring the first still image captured by the first camera, the second still image captured by the second camera, and the third still image captured by the third camera, the method further includes:

[0037] Acquire video data captured by a fourth camera, which is positioned above the comb plate and is used to capture a preset area including the comb plate.

[0038] The video data is analyzed to determine whether there are passengers currently within the preset area;

[0039] If so, return to the step of obtaining video data captured by the fourth camera and obtain the next segment of video data;

[0040] If not, then proceed with the steps of acquiring the first still image captured by the first camera, the second still image captured by the second camera, and the third still image captured by the third camera.

[0041] Optionally, when there are currently no passengers in the preset area, the following further applies:

[0042] Extract multiple frames of target images arranged in chronological order from the video data;

[0043] Select a target reference point from the step that meshes with the comb plate in the target image;

[0044] The movement trajectory of the target reference point is calculated based on the position of the target reference point in multiple frames of target images;

[0045] The movement trajectory of the target reference point is used to determine whether there is abnormal vibration in the comb plate.

[0046] Secondly, the present invention also provides an escalator comb tooth abnormality detection device, comprising:

[0047] An image acquisition module is used to acquire a first still image captured by a first camera, a second still image captured by a second camera, and a third still image captured by a third camera. The first camera is disposed at the first end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The second camera is disposed at the second end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The third camera is disposed vertically above the comb plate and captures images of the comb plate along a direction perpendicular to the comb plate.

[0048] An anomaly analysis module is used to analyze whether there are any anomalies in the first region, second region, and third region of the comb plate based on the first static image, to analyze whether there are any anomalies in the first region, second region, and third region of the comb plate based on the second static image, and to analyze whether there are any anomalies in the first region, second region, and third region of the comb plate based on the third static image, wherein the comb plate is divided into a first region, a second region, and a third region along its length, and the first region and the third region are located on both sides of the second region;

[0049] The first region anomaly determination module is used to combine the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair to determine whether there is an anomaly in the first region of the comb plate.

[0050] The second region anomaly determination module is used to combine the analysis results of the second region of the first static image pair, the analysis results of the second region of the second static image pair, and the analysis results of the second region of the third static image pair to determine whether there is an anomaly in the second region of the comb plate;

[0051] The third region anomaly determination module is used to determine whether there is an anomaly in the third region of the comb plate by combining the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair.

[0052] Thirdly, the present invention also provides an electronic device, comprising:

[0053] One or more processors;

[0054] Storage device for storing one or more programs;

[0055] When the one or more programs are executed by the one or more processors, the one or more processors implement the escalator comb tooth anomaly detection method as provided in the first aspect of the present invention.

[0056] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the escalator comb tooth anomaly detection method provided in the first aspect of the present invention.

[0057] The present invention provides a method for detecting escalator comb tooth anomalies, comprising: acquiring a first static image captured by a first camera, a second static image captured by a second camera, and a third static image captured by a third camera, wherein the first camera is disposed at the first end of the comb tooth plate and captures images of the comb tooth plate along a length direction parallel to the comb tooth plate; the second camera is disposed at the second end of the comb tooth plate and captures images of the comb tooth plate along a length direction parallel to the comb tooth plate; the third camera is disposed vertically above the comb tooth plate and captures images of the comb tooth plate along a direction perpendicular to the comb tooth plate; analyzing whether there are anomalies in the first, second, and third regions of the comb tooth plate based on the first static image; analyzing whether there are anomalies in the first, second, and third regions of the comb tooth plate based on the second static image; and analyzing whether there are anomalies in the first, second, and third regions of the comb tooth plate based on the third static image. The third region is analyzed to determine if any anomalies exist. The comb plate is divided into three regions along its length: a first region, a second region, and a third region. Regions one and three are located on either side of region two. By combining the analysis results of the first, second, and third static images of the first region, it is determined whether any anomalies exist in region one. Similarly, by combining the analysis results of the first, second, and third static images of the second region, it is determined whether any anomalies exist in region two. Finally, by combining the analysis results of the first, second, and third static images of the third region, it is determined whether any anomalies exist in region three. This invention improves the accuracy of anomaly detection by combining the analysis results of static images taken from multiple angles of a specific region of the comb plate. Furthermore, it does not require altering the original mechanical structure of the escalator, thus reducing costs.

[0058] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart of an escalator comb tooth anomaly detection method provided in an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of the structure of an escalator comb tooth anomaly detection system provided in an embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram showing the camera's placement in an embodiment of the present invention;

[0063] Figure 4 This is a schematic diagram of the region division of a comb plate provided in an embodiment of the present invention;

[0064] Figure 5 This is a schematic diagram of the structure of an escalator comb tooth abnormality detection device provided in an embodiment of the present invention;

[0065] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0066] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0068] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0069] Figure 1 This is a flowchart illustrating an escalator comb tooth anomaly detection method provided in an embodiment of the present invention. This embodiment is applicable to detecting escalator comb tooth anomalies based on image analysis. The method can be executed by an escalator comb tooth anomaly detection device provided in this embodiment of the present invention. This device can be implemented in software and / or hardware, and is typically configured in an electronic device, such as... Figure 1 As shown, the escalator comb tooth anomaly detection method may include the following steps:

[0070] S101. Acquire the first still image captured by the first camera, the second still image captured by the second camera, and the third still image captured by the third camera.

[0071] In this embodiment of the invention, the escalator may include an escalator that is inclined at a certain angle to the horizontal plane, a spiral escalator, or a horizontal pedestrian escalator, etc., and this embodiment of the invention does not limit the scope of the invention.

[0072] Figure 2 This is a schematic diagram of the structure of an escalator comb tooth anomaly detection system provided in an embodiment of the present invention. Figure 3 This is a schematic diagram showing the camera's placement in an embodiment of the present invention, as shown below. Figure 2 , 3As shown, the escalator comb tooth anomaly detection system includes multiple cameras, a video analysis terminal 150, and an escalator control center 160. All cameras are connected to the video analysis terminal 150, uploading the acquired video images to it. The video analysis terminal 150 is communicatively connected to the escalator control center 160. Specifically, the first camera (cam1) is positioned at the first end of the comb tooth plate 110 and captures images of the comb tooth plate 110 along a length parallel to its length. The second camera (cam2) is positioned at the second end of the comb tooth plate 110 and captures images of the comb tooth plate 110 along a length parallel to its length. That is, the first camera (cam1) and the second camera (cam2) are positioned opposite each other, capturing images of the comb tooth plate 110 from opposite directions. For example, the first camera (cam1) can be positioned on the skirt plate 121 at one end of the comb tooth plate 110, and the second camera (cam2) can be positioned on the skirt plate 122 at the other end of the comb tooth plate 110. The third camera cam3 is positioned directly above the comb plate 110 and captures images of the comb plate 110 in a direction perpendicular to the comb plate 110.

[0073] In this embodiment of the invention, a first still image captured by a first camera (cam1), a second still image captured by a second camera (cam2), and a third still image captured by a third camera (cam3) are acquired simultaneously. For example, the still images can be extracted from the video stream captured by the cameras; this embodiment of the invention does not impose such limitations.

[0074] S102. Analyze whether there are any abnormalities in the first, second, and third regions of the comb plate based on the first static image, analyze whether there are any abnormalities in the first, second, and third regions of the comb plate based on the second static image, and analyze whether there are any abnormalities in the first, second, and third regions of the comb plate based on the third static image.

[0075] Figure 4 This is a schematic diagram of the region division of a comb plate provided in an embodiment of the present invention, as shown below. Figure 4 As shown, in this embodiment of the invention, the comb plate is divided into a first region Q1, a second region Q2, and a third region Q3 along its length, with the first region Q1 and the third region Q3 located on either side Q2 of the second region. For example, the areas of the first region Q1, the second region Q2, and the third region Q3 are equal.

[0076] In this embodiment of the invention, the presence of anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate is analyzed based on a first static image; the presence of anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate is analyzed based on a second static image; and the presence of anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate is analyzed based on a third static image. Exemplarily, the presence of anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate can be analyzed based on traditional image morphology processing, or it can be based on machine learning algorithms. This embodiment of the invention does not limit the scope of the analysis.

[0077] For example, in some embodiments of the present invention, image morphology processing is performed on a first static image to extract comb tooth features from the first static image. The comb tooth features extracted from the first static image are compared with comb tooth features extracted from a normal comb plate image to determine whether there are any anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate.

[0078] For example, in some embodiments of the present invention, image morphology processing is performed on the second static image to extract comb tooth features. The comb tooth features extracted from the second static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate.

[0079] For example, in some embodiments of the present invention, image morphology processing is performed on a third static image to extract comb tooth features from the third static image. The comb tooth features extracted from the third static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any anomalies in the first region Q1, the second region Q2, and the third region Q3 of the comb plate.

[0080] Because the three cameras capture images of the comb plate from different directions, the image quality of each camera in the first region Q1, second region Q2, and third region Q3 of the comb plate varies due to the influence of the camera's focus area. Therefore, the reliability of the analysis results for the first region Q1, second region Q2, and third region Q3 based on the same image differs. (Example provided.) Taking a first camera (cam1) positioned near one end of the first region Q1, a second camera (cam2) positioned near one end of the third region Q3, and a third camera (cam3) positioned directly above the second region Q2 as an example, in the analysis results based on the first static image of the first camera (cam1), the confidence level of the first region Q1 is greater than that of the second region Q2, and the confidence level of the second region Q2 is greater than that of the third region Q3. In the analysis results based on the second static image of the second camera (cam2), the confidence level of the third region Q3 is greater than that of the second region Q2, and the confidence level of the second region Q2 is greater than that of the first region Q1. In the analysis results based on the third static image of the third camera (cam3), the confidence level of the second region Q2 is greater than that of the first region Q1, and the confidence level of the first region Q1 is equal to that of the third region Q3.

[0081] S103. Based on the analysis results of the first static image on the first region, the analysis results of the second static image on the first region, and the analysis results of the third static image on the first region, determine whether there is an anomaly in the first region of the comb plate.

[0082] In this embodiment of the invention, by combining the analysis results of the first static image on the first region, the analysis results of the second static image on the first region, and the analysis results of the third static image on the first region, it is determined whether there is an anomaly in the first region of the comb plate. By combining the analysis results of static images taken from multiple angles on the first region, it is determined whether there is an anomaly in the first region of the comb plate, thereby improving the accuracy of anomaly determination in the first region.

[0083] For example, based on the analysis results of the first region Q1 of the first static image, the analysis results of the first region Q1 of the second static image, and the analysis results of the first region Q1 of the third static image, a preset first confidence level, a second confidence level, and a third confidence level are assigned to the first region Q1, respectively. The sum of the first confidence level, the second confidence level, and the third confidence level is calculated to obtain the first total confidence level that the first region Q1 has an anomaly. For example, the formula for calculating the first total confidence level is as follows:

[0084] First overall confidence level = (Is there an anomaly in the first region of camera cam1? 80%: 0%) + (Is there an anomaly in the first domain of camera cam2? 40%: 0%) + (Is there an anomaly in the first domain of camera cam3? 40%: 0%).

[0085] For example, (Is there an anomaly in the first region of camera cam1? 80%:0%) means: whether there is an anomaly in the first region Q1 of the first static image of camera cam1. If yes, the first region Q1 is assigned a first confidence level of 80%; otherwise, the first region Q1 is assigned a first confidence level of 0%. (Is there an anomaly in the first region of camera cam2? 40%:0%) means: whether there is an anomaly in the first region Q1 of the second static image of camera cam2. If yes, the first region Q1 is assigned a second confidence level of 40%; otherwise, the first region Q1 is assigned a second confidence level of 0%. (Is there an anomaly in the first region of camera cam3? 40%:0%) means: whether there is an anomaly in the first region Q1 of the third static image of camera cam3. If yes, the first region Q1 is assigned a third confidence level of 40%; otherwise, the first region Q1 is assigned a third confidence level of 0%.

[0086] Determine whether the first total confidence level is greater than a preset value. For example, in some embodiments of the present invention, the preset value is set to 80%. If the first total confidence level is greater than the preset value, it is determined that the first region Q1 is abnormal; if the first total confidence level is not greater than the preset value, it is determined that the first region Q1 is not abnormal.

[0087] S104. Based on the analysis results of the first static image on the second region, the analysis results of the second static image on the second region, and the analysis results of the third static image on the second region, determine whether there is an anomaly in the second region of the comb plate.

[0088] In this embodiment of the invention, by combining the analysis results of the first static image on the second region, the analysis results of the second static image on the second region, and the analysis results of the third static image on the second region, it is determined whether there is an anomaly in the second region of the comb plate. By combining the analysis results of the static images taken from multiple angles on the second region, it is determined whether there is an anomaly in the second region of the comb plate, thereby improving the accuracy of anomaly determination in the second region.

[0089] For example, based on the analysis results of the second region Q2 of the first static image, the analysis results of the second region Q2 of the second static image, and the analysis results of the second region Q2 of the third static image, a preset fourth confidence level, a fifth confidence level, and a sixth confidence level are assigned to the second region Q2, respectively. The sum of the fourth confidence level, the fifth confidence level, and the sixth confidence level is calculated to obtain the second total confidence level that the second region Q2 is abnormal. For example, the formula for calculating the second total confidence level is as follows:

[0090] Second overall confidence level = (Is there an anomaly in the second region of camera cam1? 60%:0%) + (Is there an anomaly in the second domain of camera cam2? 60%:0%) + (Is there an anomaly in the second domain of camera cam3? 80%:0%).

[0091] For example, the meaning of the above formula is similar to that in the aforementioned steps, and will not be repeated here in the embodiments of the present invention.

[0092] Determine whether the second total confidence level is greater than a preset value. For example, in some embodiments of the present invention, the preset value is set to 80%. If the second total confidence level is greater than the preset value, it is determined that the second region Q2 is abnormal; if the second total confidence level is not greater than the preset value, it is determined that the second region Q2 is not abnormal.

[0093] S105. Based on the combined analysis results of the first static image on the third region, the analysis results of the second static image on the third region, and the analysis results of the third static image on the third region, determine whether there is an anomaly in the third region of the comb plate.

[0094] In this embodiment of the invention, by combining the analysis results of the first static image on the third region, the analysis results of the second static image on the third region, and the analysis results of the third static image on the third region, it is determined whether there is an anomaly in the third region of the comb plate. By combining the analysis results of the third region of static images taken from multiple angles, it is determined whether there is an anomaly in the third region of the comb plate, thereby improving the accuracy of anomaly determination in the third region.

[0095] For example, based on the analysis results of the third region Q3 of the first static image, the analysis results of the third region Q3 of the second static image, and the analysis results of the third region Q3 of the third static image, preset seventh confidence level, eighth confidence level, and ninth confidence level are assigned to the third region Q3, respectively. The sum of the seventh confidence level, eighth confidence level, and ninth confidence level is calculated to obtain the third total confidence level that the third region Q3 has an anomaly. For example, the formula for calculating the third total confidence level is as follows:

[0096] Third overall confidence level = (Is there an anomaly in the third region of camera cam1? 40%:0%) + (Is there an anomaly in the third domain of camera cam2? 80%:0%) + (Is there an anomaly in the third domain of camera cam3? 40%:0%).

[0097] For example, the meaning of the above formula is similar to that in the aforementioned steps, and will not be repeated here in the embodiments of the present invention.

[0098] Determine whether the third total confidence level is greater than a preset value. For example, in some embodiments of the present invention, the preset value is set to 80%. If the third total confidence level is greater than the preset value, it is determined that the third region Q3 is abnormal; if the third total confidence level is not greater than the preset value, it is determined that the third region Q3 is not abnormal.

[0099] For example, in this embodiment of the invention, if the first area Q1, the second area Q2, or the third area Q3 is found to be abnormal, the escalator control center 160 is notified and an alarm is issued by the escalator control center 160.

[0100] The escalator comb tooth anomaly detection method provided in this embodiment of the invention includes: acquiring a first static image captured by a first camera, a second static image captured by a second camera, and a third static image captured by a third camera. The first camera is positioned at the first end of the comb tooth plate and captures images of the comb tooth plate along a length direction parallel to the comb tooth plate. The second camera is positioned at the second end of the comb tooth plate and captures images of the comb tooth plate along a length direction parallel to the comb tooth plate. The third camera is positioned vertically above the comb tooth plate and captures images of the comb tooth plate along a direction perpendicular to the comb tooth plate. The method analyzes whether there are anomalies in the first, second, and third regions of the comb tooth plate based on the first static image, analyzes whether there are anomalies in the first, second, and third regions of the comb tooth plate based on the second static image, and analyzes whether there are anomalies in the first, second, and third regions of the comb tooth plate based on the third static image. The invention analyzes three regions of a comb plate. The comb plate is divided into three regions along its length: a first region, a second region, and a third region. Regions one and three are located on either side of region two. By combining the analysis results of the first, second, and third static images of the first region, the invention determines whether any anomalies exist in region one. Similarly, by combining the analysis results of the first, second, and third static images of the second region, the invention determines whether any anomalies exist in region two. Finally, by combining the analysis results of the first, second, and third static images of the third region, the invention determines whether any anomalies exist in region three. This invention improves the accuracy of anomaly detection by combining the analysis results of static images taken from multiple angles of a specific region of the comb plate. Furthermore, it does not require altering the original mechanical structure of the escalator, thus reducing costs.

[0101] In some embodiments of the present invention, such as Figure 2 , 3As shown, the escalator comb tooth anomaly detection system also includes a fourth camera cam4, which is connected to the video analysis terminal 150. For example, the fourth camera cam4 is positioned above the comb tooth plate 110 and is used to capture images of a preset area including the comb tooth plate 110. For example, it captures images of the steps 130, the comb tooth plate 110, and the bed cover 140, all of which are detached from the comb tooth plate 110. Before acquiring the first still image captured by the first camera, the second still image captured by the second camera, and the third still image captured by the third camera, the system further includes:

[0102] The system acquires video data captured by the fourth camera, analyzes the video data, and determines whether there are passengers in the preset area. If so, it returns to the step of acquiring video data captured by the fourth camera and continues to acquire the next segment of video data. If not, it executes the steps of acquiring the first static image captured by the first camera, the second static image captured by the second camera, and the third static image captured by the third camera, and begins comb plate anomaly detection to avoid passengers interfering with the comb plate anomaly detection results.

[0103] In some embodiments of the present invention, if no passengers are detected within the preset area during the above steps, dynamic analysis can be performed on the video data to analyze whether there is abnormal vibration in the comb teeth. For example, multiple frames of target images arranged chronologically are extracted from the video data. A target reference point is selected from the step meshing with the comb tooth plate in the target image. The movement trajectory of the target reference point is calculated based on its position in the multiple target images. The presence of abnormal vibration in the comb teeth is determined based on the movement trajectory of the target reference point. For example, if the movement trajectory of the target reference point deviates from the preset trajectory, it is considered that there is abnormal vibration in the comb teeth, and the escalator control center 160 is notified, which then issues an alarm.

[0104] Figure 5 This is a schematic diagram of the structure of an escalator comb tooth abnormality detection device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the escalator comb tooth abnormality detection device includes:

[0105] Image acquisition module 201 is used to acquire a first still image captured by a first camera, a second still image captured by a second camera, and a third still image captured by a third camera. The first camera is disposed at the first end of the comb plate and captures the comb plate along a length direction parallel to the comb plate. The second camera is disposed at the second end of the comb plate and captures the comb plate along a length direction parallel to the comb plate. The third camera is disposed vertically above the comb plate and captures the comb plate along a direction perpendicular to the comb plate.

[0106] Anomaly analysis module 202 is used to analyze whether there are any anomalies in the first region, second region and third region of the comb plate based on the first static image, analyze whether there are any anomalies in the first region, second region and third region of the comb plate based on the second static image, and analyze whether there are any anomalies in the first region, second region and third region of the comb plate based on the third static image, wherein the comb plate is divided into a first region, a second region and a third region along the length direction, and the first region and the third region are located on both sides of the second region;

[0107] The first region anomaly determination module 203 is used to determine whether there is an anomaly in the first region of the comb plate by combining the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair.

[0108] The second region anomaly determination module 204 is used to determine whether there is an anomaly in the second region of the comb plate by combining the analysis results of the second region of the first static image pair, the analysis results of the second region of the second static image pair, and the analysis results of the second region of the third static image pair.

[0109] The third region anomaly determination module 205 is used to determine whether there is an anomaly in the third region of the comb plate by combining the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair.

[0110] In some embodiments of the present invention, the anomaly analysis module 202 includes:

[0111] The first extraction submodule is used to perform image morphological processing on the first static image and extract comb tooth features from the first static image.

[0112] The first analysis submodule is used to compare the comb tooth features extracted from the first static image with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first region, the second region and the third region of the comb plate.

[0113] The second extraction submodule is used to perform image morphological processing on the second static image and extract comb tooth features from the second static image.

[0114] The second analysis submodule is used to compare the comb tooth features extracted from the second static image with the comb tooth features extracted from the normal comb plate image to determine whether there are any abnormalities in the first region, the second region and the third region of the comb plate.

[0115] The third extraction submodule is used to perform image morphological processing on the third static image and extract comb tooth features from the third static image.

[0116] The third analysis submodule is used to compare the comb tooth features extracted from the third static image with the comb tooth features extracted from the normal comb plate image to determine whether there are any abnormalities in the first region, second region and third region of the comb plate.

[0117] In some embodiments of the present invention, the first region anomaly determination module 203 includes:

[0118] The first confidence level determination submodule is used to assign a preset first confidence level, a second confidence level, and a third confidence level to the first region based on the analysis results of the first region of the first static image, the analysis results of the first region of the second static image, and the analysis results of the first region of the third static image.

[0119] The first calculation submodule is used to calculate the sum of the first confidence level, the second confidence level and the third confidence level to obtain the first total confidence level that the first region has an anomaly;

[0120] The first judgment submodule is used to determine whether the first total confidence level is greater than a preset value. If the first total confidence level is greater than the preset value, it is determined that there is an anomaly in the first region; if the first total confidence level is not greater than the preset value, it is determined that there is no anomaly in the first region.

[0121] In some embodiments of the present invention, the second region anomaly determination module 204 includes:

[0122] The second confidence level determination submodule is used to assign a preset fourth confidence level, fifth confidence level, and sixth confidence level to the second region based on the analysis results of the second region of the first static image, the analysis results of the second region of the second static image, and the analysis results of the second region of the third static image.

[0123] The second calculation submodule is used to calculate the sum of the fourth confidence level, the fifth confidence level, and the sixth confidence level to obtain a second total confidence level that the second region has an anomaly.

[0124] The second judgment submodule is used to determine whether the second total confidence level is greater than a preset value. If the second total confidence level is greater than the preset value, it is determined that there is an anomaly in the second region; if the second total confidence level is not greater than the preset value, it is determined that there is no anomaly in the second region.

[0125] In some embodiments of the present invention, the third region anomaly determination module 205 includes:

[0126] The third confidence level determination submodule is used to assign a preset seventh confidence level, eighth confidence level, and ninth confidence level to the third region based on the analysis results of the third region of the first static image, the analysis results of the third region of the second static image, and the analysis results of the third region of the third static image.

[0127] The third calculation submodule is used to calculate the sum of the seventh confidence level, the eighth confidence level, and the ninth confidence level to obtain the third total confidence level that the third region has an anomaly;

[0128] The third judgment submodule is used to determine whether the third total confidence level is greater than a preset value. If the third total confidence level is greater than the preset value, it is determined that the third region has an anomaly; if the third total confidence level is not greater than the preset value, it is determined that the third region does not have an anomaly.

[0129] In some embodiments of the present invention, the escalator comb tooth abnormality detection device further includes:

[0130] The video data acquisition module is used to acquire video data captured by a fourth camera before acquiring the first static image captured by the first camera, the second static image captured by the second camera, and the third static image captured by the third camera. The fourth camera is set above the comb plate and is used to capture a preset area including the comb plate.

[0131] The judgment module is used to analyze the video data and determine whether there are passengers in the preset area. If so, it returns to the step of obtaining video data captured by the fourth camera and obtains the next segment of video data; if not, it executes the steps of obtaining the first static image captured by the first camera, the second static image captured by the second camera, and the third static image captured by the third camera.

[0132] In some embodiments of the present invention, the escalator comb tooth abnormality detection device further includes:

[0133] The target image extraction module is used to extract multiple frames of target images arranged in chronological order from the video data when there are currently no passengers in the preset area.

[0134] The target reference point selection module is used to select a target reference point from the step that meshes with the comb plate in the target image;

[0135] The trajectory calculation module is used to calculate the trajectory of the target reference point based on the position of the target reference point in multiple frames of target images;

[0136] The vibration detection module is used to determine whether there is abnormal vibration in the comb plate based on the movement trajectory of the target reference point.

[0137] The aforementioned escalator comb tooth anomaly detection device can execute the multi-escalator comb tooth anomaly detection method provided in the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the escalator comb tooth anomaly detection method.

[0138] Figure 6 This is a schematic diagram of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 6 As shown, the electronic device includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Multiple components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0141] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the escalator comb tooth anomaly detection method.

[0142] In some embodiments, the escalator comb tooth anomaly detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the escalator comb tooth anomaly detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the escalator comb tooth anomaly detection method by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0147] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0148] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0149] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the escalator comb tooth anomaly detection method provided in any embodiment of this application.

[0150] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0151] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0152] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting abnormal comb teeth in escalators, characterized in that, include: Acquire a first still image captured by a first camera, a second still image captured by a second camera, and a third still image captured by a third camera. The first camera is positioned at the first end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The second camera is positioned at the second end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The third camera is positioned vertically above the comb plate and captures images of the comb plate along a direction perpendicular to the comb plate. Based on the first static image, analyze whether there are any abnormalities in the first region, second region, and third region of the comb plate; based on the second static image, analyze whether there are any abnormalities in the first region, second region, and third region of the comb plate; based on the third static image, analyze whether there are any abnormalities in the first region, second region, and third region of the comb plate. The comb plate is divided into a first region, a second region, and a third region along its length, and the first region and the third region are located on both sides of the second region. By combining the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair, it is determined whether there is an anomaly in the first region of the comb plate; By combining the analysis results of the second region of the first static image pair, the analysis results of the second region of the second static image pair, and the analysis results of the second region of the third static image pair, it is determined whether there is an anomaly in the second region of the comb plate; By combining the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair, it is determined whether there is an anomaly in the third region of the comb plate.

2. The escalator comb tooth abnormality detection method according to claim 1, characterized in that, Analyzing whether there are any anomalies in the first, second, and third regions of the comb plate based on the first static image, analyzing whether there are any anomalies in the first, second, and third regions of the comb plate based on the second static image, and analyzing whether there are any anomalies in the first, second, and third regions of the comb plate based on the third static image, includes: The first static image is subjected to image morphology processing to extract comb tooth features from the first static image. The comb tooth features extracted from the first static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first, second, and third regions of the comb plate. The second static image is subjected to image morphology processing to extract comb tooth features from the second static image; The comb tooth features extracted from the second static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first, second, and third regions of the comb plate. The third static image is subjected to image morphology processing to extract comb tooth features from the third static image; The comb tooth features extracted from the third static image are compared with the comb tooth features extracted from a normal comb plate image to determine whether there are any abnormalities in the first, second, and third regions of the comb plate.

3. The escalator comb tooth abnormality detection method according to claim 1, characterized in that, Based on the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair, it is determined whether there is an anomaly in the first region of the comb plate, including: Based on the analysis results of the first region of the first static image, the analysis results of the first region of the second static image, and the analysis results of the first region of the third static image, a preset first confidence level, a second confidence level, and a third confidence level are assigned to the first region, respectively. Calculate the sum of the first confidence level, the second confidence level, and the third confidence level to obtain the first total confidence level that the first region has an anomaly; Determine whether the first overall credibility is greater than a preset value; If the first total confidence level is greater than a preset value, then it is determined that there is an anomaly in the first region; If the first total confidence level is not greater than a preset value, then it is determined that there is no anomaly in the first region.

4. The escalator comb tooth abnormality detection method according to claim 1, characterized in that, Based on the combined analysis results of the second region of the first static image pair, the second region of the second static image pair, and the second region of the third static image pair, it is determined whether there is an anomaly in the second region of the comb plate, including: Based on the analysis results of the second region of the first static image, the analysis results of the second region of the second static image, and the analysis results of the second region of the third static image, a preset fourth confidence level, a fifth confidence level, and a sixth confidence level are assigned to the second region, respectively. The sum of the fourth confidence level, the fifth confidence level, and the sixth confidence level is calculated to obtain the second total confidence level that the second region has an anomaly; Determine whether the second overall credibility is greater than a preset value; If the second total confidence level is greater than the preset value, then the second region is determined to be abnormal; If the second total confidence level is not greater than the preset value, then it is determined that there is no anomaly in the second region.

5. The escalator comb tooth abnormality detection method according to claim 1, characterized in that, Based on the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair, it is determined whether there is an anomaly in the third region of the comb plate, including: Based on the analysis results of the third region of the first static image, the analysis results of the third region of the second static image, and the analysis results of the third region of the third static image, preset seventh confidence level, eighth confidence level, and ninth confidence level are assigned to the third region respectively. Calculate the sum of the seventh confidence level, the eighth confidence level, and the ninth confidence level to obtain the third total confidence level that the third region has an anomaly; Determine whether the third overall credibility is greater than a preset value; If the third overall confidence level is greater than a preset value, then the third region is determined to be abnormal; If the third overall confidence level is not greater than a preset value, then it is determined that there is no anomaly in the third region.

6. The escalator comb tooth abnormality detection method according to any one of claims 1-5, characterized in that, Before acquiring the first still image captured by the first camera, the second still image captured by the second camera, and the third still image captured by the third camera, the process also includes: Acquire video data captured by a fourth camera, which is positioned above the comb plate and is used to capture a preset area including the comb plate. The video data is analyzed to determine whether there are passengers currently within the preset area; If so, return to the step of obtaining video data captured by the fourth camera and obtain the next segment of video data; If not, then proceed with the steps of acquiring the first still image captured by the first camera, the second still image captured by the second camera, and the third still image captured by the third camera.

7. The escalator comb tooth abnormality detection method according to claim 6, characterized in that, When there are currently no passengers in the preset area, the following is also included: Extract multiple frames of target images arranged in chronological order from the video data; Select a target reference point from the step that meshes with the comb plate in the target image; The movement trajectory of the target reference point is calculated based on the position of the target reference point in multiple frames of target images; The movement trajectory of the target reference point is used to determine whether there is abnormal vibration in the comb plate.

8. An escalator comb tooth abnormality detection device, characterized in that, include: An image acquisition module is used to acquire a first still image captured by a first camera, a second still image captured by a second camera, and a third still image captured by a third camera. The first camera is disposed at the first end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The second camera is disposed at the second end of the comb plate and captures images of the comb plate along a length direction parallel to the comb plate. The third camera is disposed vertically above the comb plate and captures images of the comb plate along a direction perpendicular to the comb plate. An anomaly analysis module is used to analyze whether there are any anomalies in the first region, second region, and third region of the comb plate based on the first static image, to analyze whether there are any anomalies in the first region, second region, and third region of the comb plate based on the second static image, and to analyze whether there are any anomalies in the first region, second region, and third region of the comb plate based on the third static image, wherein the comb plate is divided into a first region, a second region, and a third region along its length, and the first region and the third region are located on both sides of the second region; The first region anomaly determination module is used to combine the analysis results of the first region of the first static image pair, the analysis results of the first region of the second static image pair, and the analysis results of the first region of the third static image pair to determine whether there is an anomaly in the first region of the comb plate. The second region anomaly determination module is used to combine the analysis results of the second region of the first static image pair, the analysis results of the second region of the second static image pair, and the analysis results of the second region of the third static image pair to determine whether there is an anomaly in the second region of the comb plate; The third region anomaly determination module is used to determine whether there is an anomaly in the third region of the comb plate by combining the analysis results of the third region of the first static image pair, the analysis results of the third region of the second static image pair, and the analysis results of the third region of the third static image pair.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the escalator comb tooth anomaly detection method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the escalator comb tooth anomaly detection method as described in any one of claims 1-7.

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