关键词: 图像处理, 深度学习, YOLO v3, 安全帽佩戴检测

Abstract: In production and operation sites, safety accidents caused by workers not wearing safety helmets occur from time to time. In order to reduce such incidence, a safety helmet wearing detection method based on improved YOLO v3 algorithm is proposed. Firstly, feature maps of different scales are acquired by using an image pyramid structure for position and category prediction. Secondly, the target frame parameters are clustered by using the construction site entrances and exits monitoring video as the data set. Finally, during the training process, the size of the input image is changed during the iterative process to increase the adaptability of the model to the scale. Theoretical analysis and experimental results show that in the safety helmet wearing detection task, the mAP（Mean Average Precision） reaches 92.13%, and the detection rate increases to 62 frame/s. The detection accuracy and detection rate are slightly improved compared with YOLO v3. It not only meets the real-time performance of the detection task in the safety helmet wearing test, but also has a high detection accuracy.

Key words: image processing, deep learning, YOLO v3, safety helmet wearing detection