Abstract
For the construction site with high-risk possibility, object detection based on safety helmet and reflective clothing will greatly reduce the risk of workers. At present, the algorithm based on deep learning is the mainstream algorithm of object detection. Among them, the YOLO algorithm is fast and widely used in real-time safety helmet detection. However, for the problems of small objects such as safety helmets and relatively dense detection scene objects, the detection effect is not ideal. For these problems, this paper proposes an improvement of the safety helmet detection algorithm based on YOLOv5s. The DenseBlock module is used in the improved algorithm to replace the Focus structure in the backbone network, which has an improved feature extraction capability for the network; secondly, Soft-NMS is used to retain more category frames when removing redundant frames. After the experiments, it is shown that the accuracy is improved on the homemade safety helmet dataset, which indicates the effectiveness of the improved algorithm.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 62071157, National Key Research and Development Programme 2022YFD2000500 and Natural Science Foundation of Heilongjiang Province under Grant YQ2019F011.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Z., Sun, Y., Wang, Z., Li, A. (2023). Higher Accuracy Yolov5 Based Safety Helmet Detection. In: Li, A., Shi, Y., Xi, L. (eds) 6GN for Future Wireless Networks. 6GN 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-36011-4_8
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DOI: https://doi.org/10.1007/978-3-031-36011-4_8
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