关键词: 卫星影像, 目标检测, 深度学习, 旋转矩形框

Abstract:

Recently, deep learning has developed rapidly in the field of satellite image target detection, how to accurately and effectively locate the target object is the main challenge in the research of satellite image target detection. An improved YOLOv3 algorithm based on rotating rectangular space is proposed, which can accurately locate satellite image targets. This paper adds angle conversion as a preprocessing step, which can eliminate the distortion of instance angle during network training. Two kinds of rotating bounding box coordinates are proposed for regressing network and angle anchors are added to priori boxes, which can improve the effectiveness of network detection. A novel rotated non-maximum suppression is proposed to handle rotated bounding boxes, which can effectively remove redundant rotating prediction box. The experimental results show that the improved YOLOv3 algorithm has a significant improvement over the original effect, which can effectively and accurately locate the target object of the satellite image. Improved YOLOv3 has a better visualization effect and effectively avoids the occlusion problem of prediction frames in dense scenes and the mean average precision of detection is improved by 0.8 percentage points under the premise of ensuring real-time performance.

Key words: satellite imagery, target detection, deep learning, rotating bounding box