| Abstrakt: |
Aiming at the problem of missed detection and false detection in scenarios such as complex background and dense small targets, an improved algorithm suitable for safety helmet wearing detection is proposed based on YOLOv5s. Firstly, the hybrid pooling in tandem form is used to optimize the spatial pyramid pooling (SPP) module to enhance feature extraction and feature expression and reduce the false detection rate. Then, the shallow output is added to strengthen the expression of target position information in the deep feature map, and improve the performance of small target safety helmet wearing detection. Finally, the coordinate attention mechanism (CA) is embedded in the slice module to expand the receptive field, strengthen the correlation between position information and safety helmet features, and improve the accuracy of safety helmet target detection. Experimental results show that the mAP of the improved algorithm reaches 93.50%, which is 4.28 and 5.14 percentage points higher than that of YOLOv5s and YOLOX algorithms, respectively, which meets the requirements of detecting small targets and dense targets in complex backgrounds. [ABSTRACT FROM AUTHOR] |
