关键词: 遥感图像, 目标检测, 多尺度特征融合, 加权策略

Abstract:

To solve the problems of large scale difference and low detection accuracy in remote sensing image object detection task, an improved YOLOv3 model for remote sensing image object detection based on weighted strategy is proposed. Firstly, the detection branch of feature map with smaller receptive fields is added to improve small-size object detection accuracy in remote sensing image. Secondly, anadaptive weighted fusion method for multi-scale feature maps is designed, which improves object detection accuracy by excavating representation capability of feature extraction network and fusing the multi-scale features comprehensively. Finally, a new remote sensing image object detection dataset containing four classes of objects in DIOR dataset is built, using which the proposed model is trained and tested. Experimental results show that the improved model achieves mean Average Precision（mAP） of 80.25%, which is 8.2% higher than that using the original YOLOv3 model. The trained model is also tested on RSOD, UCAS-AOD and NWPU VHR-10 datasets, which verifies the adaptability of the improved model.

Key words: remote sensing image, target detection, multi-scale features fusion, weighting strategy