Combined Channel and Spatial Attention for YOLOv5 during Target Detection
Guihui Shi, Jiezhong Huang, Junhua Zhang, Guoqin Tan, Gaoli Sang · 2021
Accuracy target detection can benefit many target detection applications. The latest YOLOv5 method has faster detection speed and better accuracy in target detection. However, there are still insufficient on bounding box positioning and it is difficult to distinguish overlapping objects. This paper proposes an improved Attention-YOLO v5, which adds channel attention and spatial attention mechanisms to the feature extraction. Furthermore, a squeeze and excitation(SE) module is applied to improve the resolution of the input image. Experiments on two public datasets show that our proposed method effectively reduces the positioning error of the bounding box and improves the detection accuracy. The accuracy on INRIA and PnPLO datasets are 97.9% and 96.2%.