Qingyang Zhou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2
CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 159-174, 2021, DOI:10.32604/cmc.2021.017480
Abstract 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… More >
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