Abstract
In order to monitor the wearing of safety helmet in dangerous workplace in real-time and ensure the safety of production, this paper proposes a method based on the yolov3 algorithm to detect the wearing of safety helmet. Firstly, K-means algorithm is used to cluster the target boxes on the self-made data set, so that the prediction boxes can fit the target better that in the data set. At the same time, the network is pre-trained on the voc2007 data set to make the model parameters more accurate and reduce the training time. Secondly, multi-scale feature extraction and multi anchor box mechanism are used to improve the accuracy of small object detection. Finally, optimizing the non-maximum suppression (NMS) algorithm with Gaussian function that can improve the detection accuracy of the occluded target. Experimental results show that the algorithm has better detection effect while meeting the real-time helmet wearing detection.
This work is supported in part by the National Natural Science Foundation of China (Nos. 61702129, 61772149, and U1701267), Innovation Project of Guet Graduate Education (No. 2019YCXS050), and Guangxi Natural Science Foundation (Nos. 2018GNSFAA138132, AD18216004, and AD18281079).
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Wen, P., Tong, M., Deng, Z., Qin, Q., Lan, R. (2020). Improved Helmet Wearing Detection Method Based on YOLOv3. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_59
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DOI: https://doi.org/10.1007/978-3-030-57884-8_59
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