Detection of Rotating Targets in Remote Sensing Images Based on Gaussian Distribution Loss
Aiming at remote sensing image target detection objects with large aspect ratio and arbitrary orientation,the general detector high accuracy detection is not high,this paper proposes a YOLOv5-based remote sensing image rotating target detection,using rotational regression Loss KLD loss,converting the rotating rectangular envelope frame into a Gaus-sian distribution,which provides a more accurate regression prediction.The realization results show that the proposed algo-rithm achieves a detection accuracy(mAP50)of 77.97%in the DOTA dataset,which is an improvement of 5.75%compared to the base model YOLOv5m.Experiments demonstrate the effectiveness of the algorithm in this paper in detecting rotating targets in remote sensing images with high accuracy.
deep learningremote sensing imagesGaussian distributionrotating target detection
万方数据
维普
