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

Abstract

AbstractThe 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.