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SCDet: decoupling discriminative representation for dark object detection via supervised contrastive learning

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Abstract

Despite the significant progress made in object detection algorithms, their potential to operate effectively under the low-light environment remains to be fully explored. Recent methods realize dark object detection on the entire representation of dark images; however, they do not further consider the potential entanglement between dark disturbance and discriminative information in dark images, and thus, the learned representation may be sub-optimal. Towards this issue, we propose supervised contrastive detection (SCDet), a novel unified framework to learn the potential composition of dark images, and decouple the discriminative component for facilitating dark object detection. Specifically, we introduce the dense decoupling contrastive (DDC) pretext task to investigate the feature consistency based on a dark transformation, allowing the learned representation to be independent of the potential entanglement to realize decoupling. Moreover, to further drive the decoupled representation to be discriminative instead of a collapse solution for dark object detection, we incorporate the supervision detection task as an extra optimization objective, resulting in the joint optimization pattern. The two tasks are complementary to each other: the DDC task regularizes the detection to learn more decoupling-friendly representation, while the supervision detection task guides the discriminative representation decoupling. As a result, the SCDet achieves dark object detection by decoding the decoupled discriminative representation of dark images. Extensive experiments on four datasets demonstrate the effectiveness of our method in both synthetic and real-world scenarios. Code is available at https://github.com/TxLin7/SCDet.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the Key Areas Research and Development Program of Guangzhou Grant 2023B01J0029, Science and technology research in key areas in Foshan under Grant 2020001006832, the Key-Area Research and Development Program of Guangdong Province under Grant 2018B010109007 and 2019B010153002, the Science and technology projects of Guangzhou under Grant 202007040006, the Guangdong Provincial Key Laboratory of Cyber-Physical System under Grant 2020B1212060069, the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515012534, and the National Statistical Science Research Project of China (No. 2022LY096).

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Authors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Tongxu Lin, Guoheng Huang & Xiaocong Huang

  2. Macao Polytechnic University, Macao, China

    Xiaochen Yuan

  3. Guangdong University of Foreign Studies, Guangzhou, China

    Guo Zhong

  4. University of Macau, Macao, China

    Chi-Man Pun

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  1. Tongxu Lin
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Correspondence to Guoheng Huang.

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Lin, T., Huang, G., Yuan, X. et al. SCDet: decoupling discriminative representation for dark object detection via supervised contrastive learning. Vis Comput 40, 3357–3369 (2024). https://doi.org/10.1007/s00371-023-03039-x

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