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On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited

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A Correction to this article was published on 06 May 2022

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Abstract

Arbitrary-oriented object detection has been a building block for rotation sensitive tasks. We first show that the boundary problem suffered in existing dominant regression-based rotation detectors, is caused by angular periodicity or corner ordering, according to the parameterization protocol. We also show that the root cause is that the ideal predictions can be out of the defined range. Accordingly, we transform the angular prediction task from a regression problem to a classification one. For the resulting circularly distributed angle classification problem, we first devise a Circular Smooth Label technique to handle the periodicity of angle and increase the error tolerance to adjacent angles. To reduce the excessive model parameters by Circular Smooth Label, we further design a Densely Coded Labels, which greatly reduces the length of the encoding. Finally, we further develop an object heading detection module, which can be useful when the exact heading orientation information is needed e.g. for ship and plane heading detection. We release our OHD-SJTU dataset and OHDet detector for heading detection. Extensive experimental results on three large-scale public datasets for aerial images i.e. DOTA, HRSC2016, OHD-SJTU, and face dataset FDDB, as well as scene text dataset ICDAR2015 and MLT, show the effectiveness of our approach.

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  1. To obtain a more thorough analysis and comprehensive results, the conference versions (Yang and Yan 2020; Yang et al. 2021a) have been significantly extended and improved in this journal version, especially in the following aspects: (i) We explore the relationship between the angle discrete representation granularity denoted by \(\omega \) and the detection performance. It shows that discrete granularity \(\omega \) can be approximated as a CSL technique with a rectangular window function, which has a certain tolerance in the divided angle interval. The difference is that CSL smooths between adjacent angle intervals. See Table 7 in Sect. 4.2; (ii) We use a specific calculation example to explain why the code length has such a large impact on the amount of detection model parameters and calculations, see Sect. 3.5; (iii) As for the angle prediction of the regression branch, we use two forms as the baseline to be compared, include direct regression and indirect regression, see Sect. 3.8; (iv) We verify our approach on additional more challenging datasets, including FDDB, and DOTA-v1.5/v2.0, see Tables 10 and 11. Among them DOTA-v1.5/v2.0 contain more data and tiny object (less than 10 pixels) than DOTA-v1.0; (v) We propose an angle fine-tuning mechanism to eliminate the theoretical prediction errors caused by angle dispersion which has been a common issue in whatever CSL and DCL, see Sect. 3.6; (vi) As a common function for downstream applications, we develop a classification-based object heading detector in Sect. 3.7. To verify its usefulness, we annotate and release a new dataset for this purpose and perform detection evaluation for both rotation and heading with a considerable amount, and more stringent evaluation indicators are used, as detailed in Sect. 4.1. To our best knowledge, this is the first public benchmark for multiple-category heading detection, especially at a considerable scale. Finally, we also release the full version of the source code.

  2. https://yangxue0827.github.io/OHD-SJTU.html.

  3. https://github.com/yangxue0827/RotationDetection.

  4. https://github.com/SJTU-Thinklab-Det/OHDet_Tensorflow.

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Acknowledgements

This work was partly supported by National Key Research and Development Program of China (2020AAA0107600), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102) and National Natural Science Foundation of China (U20B2068, 61972250). Xue Yang is partly supported by Wu Wen Jun Honorary Doctoral Scholarship, AI Institute, Shanghai Jiao Tong University.

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  1. Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xue Yang & Junchi Yan

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Yang, X., Yan, J. On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited. Int J Comput Vis 130, 1340–1365 (2022). https://doi.org/10.1007/s11263-022-01593-w

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