Journal of Guangdong University of Technology ›› 2021, Vol. 38 ›› Issue (05): 40-47.doi: 10.12052/gdutxb.200175

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Pose-based Oriented Object Detection Network for Aerial Images

Zhang Guo-sheng, Feng Guang, Li Dong   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-12-18 Online:2021-09-10 Published:2021-07-13

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Abstract: Horizontal bounding box representation in traditional object detection is not appropriate for ubiquitous oriented objects in aerial images because of the variant perspective, the crowded, cluttered and oriented objects. Therefore, a one-stage pose-based oriented object detection network is proposed, which represents oriented object as different pose and detect the oriented objects by locating the center and regressing four offsets between center and four vertices. Meanwhile, an adaptive feature pyramid network with learnable weights is utilized to automatically select more discriminative features. Moreover, according to the high resolution of aerial images, selective sampling strategy is proposed to improve the efficiency of network training and alleviate the imbalance problem of positive and negative samples. The proposed method achieves 74.85 mAP on oriented detection task of DOTA dataset, which outperforms the existing one-stage or even two-stage methods. The qualitative and quantitative comparative experiments show that the proposed pose-based oriented object detection network is simple and has competitive detection performance.

Key words: aerial image, object detection, pose, orient

CLC Number: 

  • TP391.4
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