A Novel Object Detection Method in City Aerial Image Based on Deformable Convolutional Networks

Abstract

Unmanned Aerial Vehicle (UAV) has been widely applied for aerial object detection. Recently, this issue has become a research hot spot in the field of computer vision. Currently, the performance of conventional methods to detect small objects has reached a breakthrough point. Moreover, the dense distribution and large-scale variation of objects in aerial images significantly affect the detection accuracy. In order to resolve this problem, a novel structure based on the YOLOv3 is proposed in the present study. To this end, the backbone network is replaced by ResNet_vd50 to prevent information loss in the downsampling process. Then the backbone network is modified by deformable convolution to improve the detection ability of deformed objects. In this regard, SE (Squeeze-and-Excitation) attention is embedded in the ResNet_vd block to improve the expression ability of features. Furthermore, the Soft-NMS (Soft Non Maximum Suppression) algorithm is introduced for bounding box fusion to resolve the occlusion problem. Finally, the MixUp method is used in the data augmentation stage to enrich the background information by fusing different images. Based on the obtained results, it is concluded that the proposed method has higher accuracy in aerial images than state of art object detection methods.