Improved fusion attention mechanism for detecting small and occluded targets
A modified YOLOv5s object detection algorithm is proposed to address the problem of multi-target detection in traffic environments under conditions such as insufficient nighttime lighting.This algorithm first embeds a three branch parallel convolutional attention module into the original YOLOv5s network,and achieves a lightweight and effective attention mechanism by calculating the cross dimensional attention weight matrix.Secondly,in order to solve the detection problem of small and occluded targets,a residual occlusion perception attention mechanism is embedded.The image is segmented into different convolution blocks with different kernel sizes to more accurately highlight small and occluded targets.Comparative experiments on the FL1R dataset show that this improved algorithm can improve detection accuracy in multi-object detection tasks in nighttime traffic environments,and its detection accuracy is higher than that of traditional YOLOv5s map@.5 Increase by 2.9%.