Automatic recognition and detection of building targets in urban remote sensing images using an improved regional convolutional neural network algorithm

Abstract

The accuracy of regional convolutional neural network (R-CNN) algorithms on image recognition detection remains to be improved. The authors optimised the Mask R-CNN algorithm and tested it through experiments on the automatic recognition of building targets in urban remote sensing images. It was found that the improved Mask R-CNN algorithm recognised more complete building targets and clearer edges than the original algorithm with a precision of 95.75%, a recall rate of 96.28% and a mean average precision (mAP) of 0.9403, and it also reduced the detection time per image to 0.264 s, all of which were better than other R-CNN algorithms. The ablation experiments showed that compared with the original Mask R-CNN algorithm, the improvement in the mAP of the Mask R-CNN algorithms with an improved feature pyramid network and an improved non-maximum suppression (NMS) algorithm was 0.0206 and 0.0119, respectively, while the improvement in the mAP of the improved Mask R-CNN algorithm was 0.0376. The two improvement methods adopted for the Mask R-CNN algorithm were proved to be feasible and can effectively improve the automatic recognition and detection accuracy and efficiency of building targets in urban remote sensing images.