This paper compares the effects of SE-Net and the improved CBAM attention mechanism, and proposes a CTSA (Channel and Temporal and Spatial Attention) attention mechanism that adds temporal attention for temporal features, and further proposes a TSA (Time-domain and Spatial Attention) attention mechanism that focuses on time-domain on the basis of comparing the effects of multiple attention mechanisms. The experimental results show that in the field of behaviour recognition, the TSA attention mechanism designed in this paper can achieve better recognition results when using a lighter weight network structure.
KEYWORDS: Target detection, Detection and tracking algorithms, Object detection, Safety, Convolution, Data modeling, Small targets, Performance modeling, Education and training, Windows
In order to effectively supervise the wearing of safety helmets by construction personnel, the YOLOv4-tiny target detection algorithm is used to detect the wearing of safety helmets. A lightweight model with higher accuracy and less computation is designed for YOLOv4-tiny, which is more suitable for real-time helmet wearing detection. Firstly, G-Resblock is designed to replace Resblock to reduce the computational complexity of the model and occupy less computing resources. However, YOLOv4-tiny is prone to error detection or missed detection in complex work scenarios. In order to solve this problem, an attention mechanism is added to YOLOv4-tiny, the serial channel of CBAM is improved as a parallel channel, and P-CBAM is added to YOLOv4-tiny to solve the problem of poor model detection effect. The improved YOLOv4-tiny can better complete the helmet detection task.
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