Detection algorithm for wearing safety helmet under mine based on improved YOLOv5s
Aiming at the problems of low accuracy and high missed detection rate of personnel safety helmet detection algorithm caused by complex environment under mine, an improved mine safety helmet detection algorithm based on YOLOv5s is proposed. Due to the computer system, the global context information o...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Coal Science and Technology
2025-06-01
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| Series: | Meitan kexue jishu |
| Subjects: | |
| Online Access: | http://www.mtkxjs.com.cn/article/doi/10.12438/cst.2023-1839 |
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| Summary: | Aiming at the problems of low accuracy and high missed detection rate of personnel safety helmet detection algorithm caused by complex environment under mine, an improved mine safety helmet detection algorithm based on YOLOv5s is proposed. Due to the computer system, the global context information of the image is easily lost when the convolutional neural network extracts the features, resulting in poor detection effect of the downhole small target safety helmet. To this end, the attention mechanism CBAM is fused with YOLOv5s to enhance the feature map of the target area and weaken the background information, so as to help the algorithm better locate the small target helmet. At the same time, a P2 small target detection layer is added on the basis of the original three output layers of YOLOv5s, which increases the multi-scale receptive field of the model and can capture global and local context information at the same time, which improves the detection ability of the algorithm for small targets in complex scenes. In addition, EIoU loss is utilized to replace the original CIoU loss function, solving the problem of fuzzy width-to-height ratio of the prediction frame, ensure the accuracy of the regression frame, and accelerate the convergence speed of the network. By replacing the ordinary convolutional Conv in the YOLOv5s backbone network with ShuffleNetV2, the number of model parameters is greatly reduced and the recognition speed of the model is improved. Finally, the proposed algorithm is compared with YOLOv5s, SSD, Faster-RCNN and YOLOv7 algorithms. The experimental results show that the improved algorithm is applied to the safety helmet detection of mine personnel. Compared with the original YOLOv5s, the accuracy of the proposed algorithm is increased by 2.9%, the recall rate is increased by 2.42%, and the parameter quantity is reduced by 7.6%. Finally, the average accuracy mAP@.5 of the algorithm in the mine safety helmet detection reaches 87.5%. |
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| ISSN: | 0253-2336 |