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A Shape-Based Quadrangle Detector for Aerial Images

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14428))

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Abstract

The performance of oriented object detectors has been adversely impacted by the substantial variations in object orientation. In this paper, we propose a simple but efficient object detection framework for oriented objects in aerial images, termed QuadDet. Instead of adopting oriented bounding box to represent the object, we directly predict the four vertices of the object’s quadrilateral. Specially, we introduce a fast sorting method for four vertexes of quadrangles, called the Vertex Sorting Function. The function confirms that the vertexes can compose a valid quadrangle by sorting tangents of the vertexes. Furthermore, we employ an efficient polygon IoU loss function, named the PolyIoU Loss Function, to progressively align the predicted quadrangle’s shape with the ground truth. Under these strategies, our model achieves competitive performance. Without bells and whistles, our method with ResNet50 achieves 73.63% mAP on the DOTA-v1.0 dataset running at 23.4 FPS, which surpasses all recent one-stage oriented object detectors by a significant margin. Moreover, on the largest dataset DOTA-v2.0, our QuadDet with ResNet50 obtains 51.54% mAP. The code and models are available at https://github.com/DDGRCF/QuadDet.

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Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Chaofan Rao, Wenbo Li, Xingxing Xie & Gong Cheng

Authors
  1. Chaofan Rao
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  2. Wenbo Li
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  3. Xingxing Xie
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  4. Gong Cheng
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Corresponding author

Correspondence to Gong Cheng .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rao, C., Li, W., Xie, X., Cheng, G. (2024). A Shape-Based Quadrangle Detector for Aerial Images. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_30

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  • DOI: https://doi.org/10.1007/978-981-99-8462-6_30

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  • Print ISBN: 978-981-99-8461-9

  • Online ISBN: 978-981-99-8462-6

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