YOLOv5_CSL_F: YOLOv5’s Loss Improvement and Attention Mechanism Application for Remote Sensing Image Object Detection

Abstract

With the continuous improvement of the resolution of satellite remote sensing images and aerial remote sensing images, more and more useful data and information are obtained from remote sensing images. At the same time, compared with ordinary images, remote sensing images have the characteristics of variable directions, unbalanced categories, complex backgrounds, and difficult detection of small objects. All of these make remote sensing image object detection very challenging. In this paper, based on the deep learning framework and the YOLOv5 object detection algorithm, according to the characteristics of remote sensing images, adopting Circular Smooth Label (CSL) [1] to calculate the loss of the rotating object detection bounding box and introducing the FcaNet [2] attention mechanism to design new feature fusion modules, we propose the remote sensing image object detection algorithm YOLOv5_CSL_F. We tested the algorithm model on the DOTA dataset. Compared with the detection performance of the original YOLOv5 algorithm, our algorithm improves the detection accuracy by 0.6%.