Yolo-Based Improvements in Remote Sensing Image Applications

Abstract

The identification of some specific targets in remote sensing images is still quite challenging despite the adequate accuracy of deep learning-based target detection models. This work proposes a variant of YOLOv3 based on the residual structure as the backbone and the attention mechanism module, which improves the ability of YOLOv3 to extract features. SGE is a lightweight module that can fully extract features from images without bringing an increase in computation. Furthermore, the dilated encoder module used in YOLOF was introduced as a neck to enrich the perceptual field of the C5 feature layer by concatenating four layers of dilated convolution with different expansion coefficients. The C5 feature layer and the residual structure were further processed to contain sufficient scale information for further detection. In terms of the mean average precision (mAP), experimental results demonstrate that the proposed model outperforms the other models: YOLOv3, faster-RCNN-r50+GACL Net, and YOLOv4.