Attention Guided YOLOv3 for Wearing Safety Helmet Detection
Shanshan Huang, Jianhui Huang, Yongqiang Kong · 2020
Wearing safety helmet is of great importance to ensure the personal safety in power substations. The task of safety helmet detection still requires manual effort which is time-consuming and laborious. To address this problem, this paper introduces a computer vision method for automatic safety helmet detection based on YOLOv3 algorithm. We propose an attention mechanism which contains spatial-wise and channel-wise attention modules, to separately enhance the low-level and high-level features of deep convolutional encoder. Equipping YOLOv3 with our attention mechanism, the detector can improve its ability of detecting small objects, which is suitable for real-world safety helmet detection. Experiments on a publicly available helmet wearing detection dataset show that the proposed method is able to achieve good performance while running at a high speed.