A method, system, device and storage medium for detecting the correct wearing of a safety helmet at a construction site

Abstract

The present invention discloses a method, system, device, and storage medium for detecting the correct wearing of safety helmets at construction sites. These methods are applicable to the field of construction, and include: performing de-reflection processing on a test image based on grayscale contrast and preprocessing using three RGB channels; obtaining a face image to be identified from the test image after de-reflection processing, and matching the identity of the face image to be identified with the corresponding safety helmet wearing standard based on a multi-task regression face and posture feature recognition model; inputting the head image corresponding to the face image to be identified with a successful identity match into a safety helmet wearing detection model based on YOLOv4, and outputting a safety helmet wearing detection result; and based on the safety helmet wearing detection result, using RGB three-channel pixel value comparison to determine whether the safety helmet wearing of the face image to be identified is consistent with the safety helmet wearing standard corresponding to the matched identity, and outputting a safety helmet correct wearing detection result. The present invention effectively improves the accuracy of safety helmet wearing detection.

Description

Technical Field

[0001] The present invention relates to the field of building construction, and in particular to a method, system, equipment and storage medium for detecting the correct wearing of a safety helmet at a building construction site. Background Art

[0002] Properly wearing hard hats at construction sites has always been a core requirement in the construction industry. Properly wearing hard hats not only clearly identifies the division of labor among construction workers but also significantly protects the lives of different types of workers. However, most grassroots workers at construction sites lack this awareness, resulting in a complex division of labor. Furthermore, some fail to wear hard hats, effectively preventing their own safety. Furthermore, construction site supervisors are unable to provide 24 / 7, comprehensive monitoring and reminders. Therefore, how to automatically detect the wearing of hard hats has been a hotly debated topic.

[0003] Existing automatic helmet detection methods typically use cameras to capture real-time construction site conditions and use image recognition technology to determine whether workers are wearing helmets. This technology has the following drawbacks: Construction sites often feature a high concentration of reflective materials, which can easily lead to misjudgments due to image reflections; it cannot effectively identify workers belonging to the same construction unit, potentially detecting people or objects that do not belong to the unit; the features extracted during helmet recognition are limited, and recognition accuracy needs to be improved; and it can only identify whether workers are wearing helmets, but cannot determine whether the helmets worn by different types of workers meet the appropriate wearing standards for their respective roles.

[0004] To this end, how to provide a method, system, equipment and storage medium for detecting the correct wearing of safety helmets for construction sites that can effectively solve the problems in existing safety helmet wearing detection technology that are affected by image reflection and single feature extraction, as well as the inability to effectively judge the staff of the construction unit and the inability to identify whether the safety helmets worn by different types of staff meet the wearing standards of their identities is an issue that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention proposes a method, system, device and storage medium for detecting the correct wearing of a safety helmet at a construction site.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for detecting whether a safety helmet is correctly worn at a construction site comprises:

[0008] Step 1: Based on the grayscale contrast and preprocessing of the RGB three channels, the image to be tested is de-reflected;

[0009] Step 2: Obtain the face image to be identified from the de-reflected image to be tested, and match the identity of the face image to be identified with the corresponding helmet wearing standard based on the multi-task regression face and posture feature recognition model;

[0010] Step 3: Input the head image corresponding to the face image to be identified with successful identity matching into the helmet wearing detection model based on YOLOv4, and output the helmet wearing detection result;

[0011] Step 4: Based on the helmet wearing detection results, use the RGB three-channel pixel value comparison to determine whether the helmet wearing of the face image to be identified is consistent with the helmet wearing standard corresponding to the matched identity, and output the helmet correct wearing detection result.

[0012] Optionally, in step 1, de-reflection processing is performed on the image to be tested based on grayscale contrast and preprocessing of the RGB three channels, specifically:

[0013] Decompose the image to be tested into RGB three channels and obtain grayscale images of the three RGB channels respectively;

[0014] Compare the grayscale values of the RGB three-channel grayscale images under the same pixel, combine the pixel points of the channel grayscale image with the smallest grayscale value under each pixel, and obtain the reflective image to be calibrated;

[0015] Compare the grayscale value of each pixel in the reflective image to be calibrated with the preset reflective grayscale threshold, set the grayscale value of the pixel greater than the preset reflective grayscale threshold to 70 and calibrate it as white, and set the grayscale value of the pixel less than or equal to the preset reflective grayscale threshold to 0 and calibrate it as black, and obtain the filtered reflective image;

[0016] The image to be tested is subtracted from each pixel in the filtered reflection image to obtain the image to be tested after de-reflection processing.

[0017] Optionally, in step 2, before the face and posture feature recognition model based on multi-task regression matches the identity of the face image to be identified and the corresponding helmet wearing standard, it also includes: a segmentation feature extraction network based on the image segmentation model and an image classification network to obtain face pixels and sub-face pixels to determine the face area.

[0018] Optionally, in step 2, the face and posture feature recognition model based on multi-task regression includes: a face feature recognition network, a face posture feature recognition network, a distance posture recognition network, and an angle posture recognition network;

[0019] Input the face image to be recognized into the face feature recognition network and the posture feature recognition network respectively to obtain face feature information and posture feature information;

[0020] The posture feature information is input into the distance posture recognition network and the angle posture recognition network respectively to obtain the distance posture information and angle posture information under the posture feature information, which are combined with the facial feature information to obtain the facial feature information corresponding to the distance posture information and angle posture information under the posture feature information, so as to perform identity matching in the facial feature database.

[0021] Optionally, in step 3, the helmet wearing detection model based on YOLOv4 is as follows:

[0022] The SPPNet network is used to extract contextual features of head images using multiple maximum pooling methods at different scales.

[0023] The PANet network is used to perform parameter fusion on multiple feature maps of different scales output by the SPPNet network through convolution, up-sampling, down-sampling, feature fusion and stacking.

[0024] Safety helmet wearing detection is performed based on the features after parameter fusion.

[0025] Optionally, the pooling kernels for maximum pooling include: 13x13, 9x9, 5x5, and 1x1.

[0026] Optionally, in step 4, based on the helmet wearing detection result, the RGB three-channel pixel value comparison is used to determine whether the helmet wearing of the face image to be identified is consistent with the helmet wearing standard corresponding to the matched identity, specifically:

[0027] The feature extraction network is used to output the helmet wearing detection result as the head image with the helmet worn, and the helmet is cropped;

[0028] Obtain the RGB three-channel pixel value at the center of the cropped helmet image, and subtract it from the RGB three-channel pixel value of the helmet wearing standard corresponding to the matched identity of the face image to be identified. If the difference is within the preset error threshold, it is judged as consistent; if the difference is not within the preset error threshold, it is judged as inconsistent.

[0029] The present invention also provides a safety helmet correct wearing detection system for construction sites using a safety helmet correct wearing detection method for construction sites, comprising:

[0030] De-reflection module: used for grayscale contrast and pre-processing based on RGB three channels to perform de-reflection processing on the image to be tested;

[0031] Identity matching and helmet wearing standard recognition module: This module obtains the face image to be recognized from the de-reflection processed image to be tested, and matches the identity of the face image to be recognized with the corresponding helmet wearing standard based on the multi-task regression face and posture feature recognition model;

[0032] Helmet wearing detection module: The head image corresponding to the face image to be identified with successful identity matching is input into the helmet wearing detection model based on YOLOv4, and the helmet wearing detection result is output;

[0033] Safety helmet correct wearing detection module: Based on the safety helmet wearing detection results, the module uses the RGB three-channel pixel value comparison to determine whether the safety helmet wearing of the face image to be identified is consistent with the safety helmet wearing standard corresponding to the matched identity, and outputs the safety helmet correct wearing detection results.

[0034] The present invention further provides an electronic device, comprising:

[0035] memory for storing computer programs;

[0036] The processor is used to implement the steps of a method for detecting the correct wearing of a safety helmet at a construction site when executing a computer program.

[0037] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of a method for detecting the correct wearing of a safety helmet at a construction site are implemented.

[0038] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention proposes a method, system, equipment and storage medium for detecting the correct wearing of safety helmets at construction sites. Through grayscale contrast and preprocessing based on RGB three channels, the problem of misjudgment caused by the large amount of reflective materials on construction sites, which is easy to cause image reflection, is solved; through the face and posture feature recognition model based on multi-task regression, the identity of the face image to be identified and the corresponding safety helmet wearing standard are matched. On the basis of solving the problem that the existing technology cannot effectively judge the staff of the construction unit and may detect other personnel or objects that do not belong to the construction unit, the face recognition accuracy is further improved; through the safety helmet wearing detection model based on YOLOv4, multiple contextual features of maximum pooling at different scales are extracted and fused, effectively improving the safety helmet wearing recognition accuracy; by using the RGB three-channel pixel value comparison, it is judged whether the safety helmet wearing of the face image to be identified is consistent with the safety helmet wearing standard corresponding to the matched identity, solving the problem of being unable to identify whether the safety helmets worn by different types of staff meet the wearing standards of their identities. And through the above technical features, the present invention conducts three gradual inspections, namely, inspection of non-construction unit staff, inspection of safety helmets not being worn, and inspection of safety helmets not being worn correctly, which further improves the accuracy of safety helmet inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Example 1:

[0043] Embodiment 1 of the present invention discloses a method for detecting the correct wearing of a safety helmet at a construction site. Figure 1 Shown, including:

[0044] Step 1: Based on the grayscale contrast and preprocessing of the RGB three channels, the image to be tested is de-reflected.

[0045] Based on the grayscale contrast and preprocessing of the RGB three channels, the image to be tested is de-reflected, specifically:

[0046] Decompose the image to be tested into RGB three channels and obtain grayscale images of the three RGB channels respectively;

[0047] Compare the grayscale values of the RGB three-channel grayscale images under the same pixel, combine the pixel points of the channel grayscale image with the smallest grayscale value under each pixel, and obtain the reflective image to be calibrated;

[0048] Compare the grayscale value of each pixel in the reflective image to be calibrated with the preset reflective grayscale threshold (taken as 210), set the grayscale value of the pixel greater than the preset reflective grayscale threshold to 70 and calibrate it as white (reflective part), and set the grayscale value of the pixel less than or equal to the preset reflective grayscale threshold to 0 and calibrate it as black (non-reflective part), and obtain the filtered reflective image;

[0049] The image to be tested is subtracted from each pixel in the filtered reflection image to obtain the image to be tested after de-reflection processing.

[0050] In order to avoid the color of the reflective part being too dark, which is not conducive to subsequent de-reflection processing, after calibration into reflective parts and non-reflective parts, it also includes: expanding all pixels of the calibrated image by 0.5-0.7 times to obtain a filtered reflective image.

[0051] Step 2: Obtain the face image to be identified from the de-reflected image. Based on the multi-task regression facial and posture feature recognition model, match the identity of the face image to the corresponding helmet wearing standard. (At this point, the first detection is completed, and the presence of non-construction personnel entering the building is reported.)

[0052] Before the face and posture feature recognition model based on multi-task regression matches the identity of the face image to be identified and the corresponding helmet wearing standard, it also includes: a segmentation feature extraction network based on the image segmentation model and an image classification network to obtain face pixels and sub-face pixels to determine the face area.

[0053] Face and posture feature recognition model based on multi-task regression, including: face feature recognition network, face posture feature recognition network, distance posture recognition network and angle posture recognition network;

[0054] Input the face image to be recognized into the face feature recognition network and the posture feature recognition network respectively to obtain face feature information and posture feature information;

[0055] The posture feature information is input into the distance posture recognition network and the angle posture recognition network respectively to obtain the distance posture information and angle posture information under the posture feature information, which are combined with the facial feature information to obtain the facial feature information corresponding to the distance posture information and angle posture information under the posture feature information, so as to perform identity matching in the facial feature database.

[0056] Step 3: Input the head image corresponding to the face image to be identified that has successfully matched the identity into the helmet wearing detection model based on YOLOv4, and output the helmet wearing detection result. (At this point, the second detection is completed and the situation where XXX is not wearing a helmet is reported)

[0057] The helmet wearing detection model based on YOLOv4 is as follows:

[0058] The SPPNet network is used to extract contextual features of head images using multiple maximum pooling methods at different scales.

[0059] The PANet network is used to perform parameter fusion on multiple feature maps of different scales output by the SPPNet network through convolution, up-sampling, down-sampling, feature fusion and stacking.

[0060] Safety helmet wearing detection is performed based on the features after parameter fusion.

[0061] The pooling kernels of maximum pooling include: 13x13, 9x9, 5x5, and 1x1.

[0062] Step 4: Based on the helmet wearing detection results, the RGB three-channel pixel value comparison is used to determine whether the helmet wearing of the face image to be identified is consistent with the helmet wearing standard corresponding to the matched identity, and the correct helmet wearing detection result is output. (At this point, the third test is completed and the situation of XXX not wearing the helmet correctly is reported)

[0063] Based on the helmet wearing detection results, the RGB three-channel pixel value comparison is used to determine whether the helmet wearing of the face image to be identified is consistent with the helmet wearing standard corresponding to the matched identity. Specifically:

[0064] The feature extraction network is used to output the helmet wearing detection result as the head image with the helmet worn, and the helmet is cropped;

[0065] Obtain the RGB three-channel pixel value at the center of the cropped helmet image, and subtract it from the RGB three-channel pixel value of the helmet wearing standard corresponding to the matched identity of the face image to be identified. If the difference is within the preset error threshold, it is judged as consistent; if the difference is not within the preset error threshold, it is judged as inconsistent.

[0066] Example 2:

[0067] Embodiment 2 of the present invention discloses a safety helmet correct wearing detection system for construction sites using a safety helmet correct wearing detection method for construction sites, comprising:

[0068] De-reflection module: used for grayscale contrast and pre-processing based on RGB three channels to perform de-reflection processing on the image to be tested;

[0069] Identity matching and helmet wearing standard recognition module: This module obtains the face image to be recognized from the de-reflection processed image to be tested, and matches the identity of the face image to be recognized with the corresponding helmet wearing standard based on the multi-task regression face and posture feature recognition model;

[0070] Helmet wearing detection module: The head image corresponding to the face image to be identified with successful identity matching is input into the helmet wearing detection model based on YOLOv4, and the helmet wearing detection result is output;

[0071] Safety helmet correct wearing detection module: Based on the safety helmet wearing detection results, the module uses the RGB three-channel pixel value comparison to determine whether the safety helmet wearing of the face image to be identified is consistent with the safety helmet wearing standard corresponding to the matched identity, and outputs the safety helmet correct wearing detection results.

[0072] Example 3:

[0073] Embodiment 3 of the present invention discloses an electronic device, including:

[0074] memory for storing computer programs;

[0075] The processor is used to implement the steps of a method for detecting the correct wearing of a safety helmet at a construction site when executing a computer program.

[0076] Example 4:

[0077] Embodiment 4 of the present invention discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of a method for detecting the correct wearing of a safety helmet at a construction site are implemented.

[0078] The embodiments of the present invention disclose a method, system, device, and storage medium for detecting the correct wearing of safety helmets at construction sites. By using grayscale contrast and preprocessing based on RGB three channels, the problem of misjudgment caused by image reflections due to the large amount of reflective materials at construction sites is solved. By using a face and posture feature recognition model based on multi-task regression, the identity of the face image to be identified is matched with the corresponding safety helmet wearing standard. This not only solves the problem that the existing technology cannot effectively judge the staff of the construction unit and may detect other personnel or objects that do not belong to the construction unit, but also further improves the accuracy of face recognition. By using a safety helmet wearing detection model based on YOLOv4, multiple contextual features of maximum pooling at different scales are extracted and integrated, effectively improving the accuracy of safety helmet wearing recognition. By using RGB three-channel pixel value comparison to determine whether the safety helmet wearing of the face image to be identified is consistent with the safety helmet wearing standard corresponding to the matched identity, the problem of being unable to identify whether the safety helmets worn by different types of staff meet the wearing standards of their identities is solved. And through the above technical features, the present invention conducts three gradual inspections, namely, inspection of non-construction unit staff, inspection of safety helmets not being worn, and inspection of safety helmets not being worn correctly, which further improves the accuracy of safety helmet inspection.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0080] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting whether a safety helmet is worn correctly at a construction site, characterized in that: include: Step 1: Based on the grayscale contrast and preprocessing of the RGB three channels, the image to be tested is de-reflected; Step 2: Obtain a face image to be identified in the image to be tested after de-reflection processing, and match the identity of the face image to be identified with the corresponding helmet wearing standard based on a multi-task regression face and posture feature recognition model; Step 3: Input the head image corresponding to the face image to be identified with successful identity matching into the helmet wearing detection model based on YOLOv4, and output the helmet wearing detection result; Step 4: Based on the helmet wearing detection result, use the RGB three-channel pixel value comparison to determine whether the helmet wearing of the face image to be identified is consistent with the helmet wearing standard corresponding to the matched identity, and output the helmet correct wearing detection result.

2. A method for detecting the correct wearing of a safety helmet for a construction site according to claim 1, characterized in that: In step 1, based on the grayscale contrast and preprocessing of the RGB three channels, the image to be tested is subjected to de-reflection processing, specifically: Decomposing the image to be tested into RGB three-channels to obtain grayscale images of the three RGB channels respectively; Compare the grayscale values of the RGB three-channel grayscale images under the same pixel, combine the pixel points of the channel grayscale image with the smallest grayscale value under each pixel, and obtain the reflective image to be calibrated; Comparing the grayscale value of each pixel in the reflective image to be calibrated with a preset reflective grayscale threshold, setting the grayscale value of pixels greater than the preset reflective grayscale threshold to 70 and calibrating them as white, and setting the grayscale value of pixels less than or equal to the preset reflective grayscale threshold to 0 and calibrating them as black, to obtain a filtered reflective image; Subtract each pixel in the image to be tested from that in the filtered reflected image to obtain the image to be tested after de-reflection processing.

3. A method for detecting the correct wearing of a safety helmet at a construction site according to claim 1, characterized in that: In step 2, before the face and posture feature recognition model based on multi-task regression matches the identity of the face image to be identified and the corresponding helmet wearing standard, it also includes: a segmentation feature extraction network based on the image segmentation model and an image classification network to obtain face pixels and sub-face pixels to determine the face area.

4. A method for detecting the correct wearing of a safety helmet at a construction site according to claim 1, characterized in that: In step 2, the face and posture feature recognition model based on multi-task regression includes: a face feature recognition network, a face posture feature recognition network, a distance posture recognition network, and an angle posture recognition network; Inputting the face image to be recognized into the face feature recognition network and the posture feature recognition network respectively to obtain face feature information and posture feature information; The posture feature information is respectively input into the distance posture recognition network and the angle posture recognition network to obtain the distance posture information and the angle posture information under the posture feature information, and is combined with the facial feature information to obtain the facial feature information corresponding to the distance posture information and the angle posture information under the posture feature information, so as to perform identity matching in the facial feature database.

5. The method for detecting the correct wearing of a safety helmet at a construction site according to claim 1, wherein: In step 3, the helmet wearing detection model based on YOLOv4 is specifically: Using the SPPNet network to perform multiple maximum pooling context feature extractions at different scales on the head image; The PANet network is used to perform parameter fusion on multiple feature maps of different scales output by the SPPNet network through convolution, up-sampling, down-sampling, feature fusion and stacking; Safety helmet wearing detection is performed based on the features obtained by fusion of the parameters.

6. A method for detecting the correct wearing of a safety helmet at a construction site according to claim 5, characterized in that: The pooling kernels of the maximum pooling include: 13x13, 9x9, 5x5, and 1x1.

7. A method for detecting the correct wearing of a safety helmet at a construction site according to claim 1, characterized in that: In step 4, based on the helmet wearing detection result, the RGB three-channel pixel value comparison is used to determine whether the helmet wearing of the face image to be identified is consistent with the helmet wearing standard corresponding to the matched identity, specifically: Using a feature extraction network to perform helmet cropping on the head image where the helmet wearing detection result is output as a helmet wearing head image; Obtain the RGB three-channel pixel value at the center of the cropped helmet image, and subtract it from the RGB three-channel pixel value of the helmet wearing standard corresponding to the matched identity of the face image to be identified. If the difference is within the preset error threshold, it is determined to be consistent; if the difference is not within the preset error threshold, it is determined to be inconsistent.

8. A safety helmet correct wearing detection system for construction sites using the safety helmet correct wearing detection method for construction sites according to any one of claims 1 to 7, characterized in that: include: De-reflection module: used for grayscale contrast and pre-processing based on RGB three channels to perform de-reflection processing on the image to be tested; Identity matching and helmet wearing standard recognition module: obtains the face image to be recognized in the image to be tested after de-reflection processing, and matches the identity of the face image to be recognized and the corresponding helmet wearing standard based on the face and posture feature recognition model of multi-task regression; Helmet wearing detection module: inputs the head image corresponding to the face image to be identified with successful identity matching into the helmet wearing detection model based on YOLOv4, and outputs the helmet wearing detection result; Safety helmet correct wearing detection module: Based on the safety helmet wearing detection result, the RGB three-channel pixel value comparison is used to determine whether the safety helmet wearing of the face image to be identified is consistent with the safety helmet wearing standard corresponding to the matched identity, and output the safety helmet correct wearing detection result.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of a method for detecting the correct wearing of a safety helmet at a construction site as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for detecting the correct wearing of a safety helmet at a construction site as described in any one of claims 1 to 7.

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