CBAM-Yolov5: improved Yolov5 based on attention model for infrared ship detection
Lize Miao, Ning Li, Minglong Zhou, Huiyu Zhou
Abstract
Infrared image has the low contrast and resolution, and there are few available features for infrared small targets, so the quality of bounding box obtained by the target detection model is substandard. To solve the problems, a model named CBAM-Yolov5 is proposed. Firstly, the convolutional block attention module is added to enhance the feature extraction ability of the backbone network. Then a scale bias factor is designed to improve the regression effect on the bounding box of small targets, which will increase the weight of the small targets on the loss function. Through experimental verification, the mAP and recall rate of our model can be 93.3% and 90.4% on the infrared ship dataset, and compared with Yolov5, it has increased by 1.1% and 2.0%, respectively.