关键词: 安全帽检测, YOLOv3, 深度可分离卷积, 特征融合, [K]-Means

Abstract:

Aiming at the problem of low accuracy and slow detection rate of helmet wearing detection in intelligent monitoring, a helmet wearing detection algorithm YOLOv3-WH based on improved YOLOv3（You Only Look Once） is proposed. The network structure on the basis of the YOLOv3 algorithm is improved, the scale of the input image is increased. Using deep separable convolution structure can replace the Darknet-53 traditional convolution, reduce the loss of features, model parameters, and increase the detection rate. Using multi-scale feature detection can increase the shallow detection scale, add 4 times the up-sampling feature fusion structure and improve the accuracy of helmet wearing detection. Optimizing the [K]-Means clustering algorithm can obtain the anchor box of the helmet wearing detection, according to the predicted scale size allocate suitable anchors, the model training and detection rate are improved. Experimental results show that compared with YOLOv3, YOLOv3-WH has a 64% increase in frame detection per second（FPS）, and an average detection accuracy（mAP） has increased by 6.5%. The algorithm improves the detection rate of helmet wearing while improving detection. The accuracy rate is of certain practicality for the detection of helmet wearing.

Key words: helmet wear detection, YOLOv3, deep separable convolution, feature fusion, [K]-Means