Multi-scale normalized detection method for airborne wide-area remote sensing images
Aiming at the difficulty of object detection caused by the large target size variation,complex background noise and dense targets in airborne wide-area remote sensing images,this paper unifies the target pixel size of the input image by optimizing the segmentation method,and proposes a multi-scale normalized convolutional neural networks model(MNNet).To enhance the feature correlation between localities,this paper designs a space global connection block(SGC),which effectively improves the detection accuracy.For the problem that the parameters of the existing NMS algorithm depend on the empirical setting,this paper proposes a self-adaption non-maxima suppression method(DNMS),which reduces the difficulty of model deployment.The test results on the RSF dataset show that the average precision(AP)of the model in this paper is higher than that of other models by more than 5.0％,and the detection speed reaches 57.7 fps,which can meet the detection task of remote sensing images.
万方数据
维普
