Object detection of high-resolution remote sensing images by neural architecture search
Aiming at the problems of traditional object detection methods on remote sensing images based on deep learning networks need hand-crafted architectures,which are overly dependent on expert experience and time-consuming,an object detection method for remote sensing images based on neural architecture search is proposed.The network is automatically built by pathwise sampling and evolutionary search strategy for object detection of remote sensing images.Experiments on DIOR dataset and RSOD dataset show that the mean average precision of object detection reached 67.8%and 85.5%,and the FLOPs are 208.47 G and 201.67 G,which are better than the network models such as Faster R-CNN,RetinaNet,NAS-FCOS,ResNet Strikes Back,HRNet and GRoIE in terms of detection accuracy and computational efficiency.The proposed method can automatically search the network architecture for object detection of high-resolution remote sensing images,which is superior to the hand-crafted classical networks.
万方数据
维普
