The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements, but they can’t accurately detect small objects and objects with obstructions. Therefore, we propose a helmet detection algorithm based on the attention mechanism (AT-YOLO). First of all, a channel attention module is added to the YOLOv3 backbone network, which can adaptively calibrate the channel features of the direction to improve the feature utilization, and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the network. Secondly, we use DIoU (Distance Intersection over Union) bounding box regression loss function, it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes, which makes the network more accurate in detecting small objects and faster in convergence. Finally, we explore the training strategy of the network model, which improves network performance without increasing the inference cost. Experiments show that the mAP of the proposed method reaches 96.5%, and the detection speed can reach 27 fps. Compared with other existing methods, it has better performance in detection accuracy and speed.

Safety helmet detection; attention mechanism; convolutional neural network; training strategies