Target Detection Algorithm Trans_YOLOv5 Based on Aerial Image
Compared with the detection algorithm of natural images,there are problems such as random target angle,sharp change of target scale,dense small targets,and complex image background in aerial image target detection.Trans-YOLOv5 algorithm suitable for aerial image detection is proposed to solve this series of problems.Modifying the data preprocessing module and post-processing method in the YOLOv5 algorithm to add the processing of a target angle label to make it suitable for aerial images with random target angles.CSL(Circular Smooth Label)is introduced to transform the label angle regression issue into a classification issue about the problem of boundary problems.Regarding the issue of small target detection in aerial images,we integrate Swin Transformer into the YOLOv5 framework to capture global semantic information,which improve the detection effect of the model on small targets,and cooperate with the attention mechanism module to improve the global representation ability,so that the network model pays more attention to the target object to be detected.The experimental results on the DOTAv2.0 dataset validate the effectiveness of the proposed method.The detection results reach 60.98%mAP,which is 10.85 percentage points higher than that of the original YOLOv5 algorithm and 2.01 per-centage points higher than the competition results published on the official website.
small target detectionaerial imagesYOLOv5circular smooth labelSwin Transformer
国家科技期刊平台
NSTL
万方数据
维普
