Improved smoking behavior detection algorithm based on YOLOv5s

Due to the limitations of monitoring equipment in actual non-smoking places and the complexity of the background in public places, the pictures taken often have problems such as too small detection target scale, too low resolution, and too many background interference factors. Mainstream detectors are prone to miss detection and false detection in practical applications, resulting in poor detection results. To solve the above problems, this paper proposes an improved SMYOLOv5s based on YOLOv5s. In the feature extraction stage, the convolutional attention module (CBAM) is integrated to enhance the expression of small object features. In the feature fusion stage, Swin Transformer Block module and BiFPN structure are introduced to effectively improve the model missing detection problem. On top of this, an additional smallsize detection head is added to further strengthen the fusion of shallow information of small object and improve the detection accuracy. Experimental results show that the improved algorithm has a detection accuracy of 90.9% for smoking behavior. Compared with the pre-improved algorithm, the mAP increased by 3.8%. The detection effect is significantly improved and has high application value.