Airplane Detection of Optical Remote Sensing Images Based on Deep Learning

Target detection for optical remote sensing images has always been one of the hotspots in the field of remote sensing. However, the accuracy of the existing detection methods for targets with complex background and small size is low. Aiming at the problem, a target detection method based on Mask-RCNN framework is proposed. The algorithm uses ResNet50 as the feature extraction network and uses the feature reuse technology to realize better extraction of the semantic features of the target. In view of the fact that the size ratio of different types of aircrafts is not fixed, a set of more suitable candidate frame scales is designed. The experimental results show that this method has higher detection accuracy for small object detection compared with the previous detection algorithms.