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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 3: AAAI-22 Technical Tracks 3

Polygon-to-Polygon Distance Loss for Rotated Object Detection

February 1, 2023

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Abstract:

There are two key issues that limit further improvements in the performance of existing rotational detectors: 1) Periodic sudden change of the parameters in the rotating bounding box (RBBox) definition causes a numerical discontinuity in the loss (such as smoothL1 loss). 2) There is a gap of optimization asynchrony between the loss in the RBBox regression and evaluation metrics. In this paper, we define a new distance formulation between two convex polygons describing the overlapping degree and non-overlapping degree. Based on this smooth distance, we propose a loss called Polygon-to-Polygon distance loss (P2P Loss). The distance is derived from the area sum of triangles specified by the vertexes of one polygon and the edges of the other. Therefore, the P2P Loss is continuous, differentiable, and inherently free from any RBBox definition. Our P2P Loss is not only consistent with the detection metrics but also able to measure how far, as well as how similar, a RBBox is from another one even when they are completely non-overlapping. These features allow the RetinaNet using the P2P Loss to achieve 79.15% mAP on the DOTA dataset, which is quite competitive compared with many state-of-the-art rotated object detectors.

Authors

Yang Yang

Shenzhen University


Jifeng Chen

Shenzhen University


Xiaopin Zhong

Shenzhen University


Yuanlong Deng

Shenzhen University


DOI:

10.1609/aaai.v36i3.20214


Topics: AAAI

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HOW TO CITE:

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng Polygon-to-Polygon Distance Loss for Rotated Object Detection Proceedings of the AAAI Conference on Artificial Intelligence, 36 (2022) 3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng Polygon-to-Polygon Distance Loss for Rotated Object Detection AAAI 2022, 3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng (2022). Polygon-to-Polygon Distance Loss for Rotated Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng. Polygon-to-Polygon Distance Loss for Rotated Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 36 2022 p.3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng. 2022. Polygon-to-Polygon Distance Loss for Rotated Object Detection. "Proceedings of the AAAI Conference on Artificial Intelligence, 36". 3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng. (2022) "Polygon-to-Polygon Distance Loss for Rotated Object Detection", Proceedings of the AAAI Conference on Artificial Intelligence, 36, p.3072-3080

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng, "Polygon-to-Polygon Distance Loss for Rotated Object Detection", AAAI, p.3072-3080, 2022.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng. "Polygon-to-Polygon Distance Loss for Rotated Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2022, p.3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng. "Polygon-to-Polygon Distance Loss for Rotated Object Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 36, (2022): 3072-3080.

Yang Yang||Jifeng Chen||Xiaopin Zhong||Yuanlong Deng. Polygon-to-Polygon Distance Loss for Rotated Object Detection. AAAI[Internet]. 2022[cited 2023]; 3072-3080.


ISSN: 2374-3468


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