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LLD-YOLO: a multi-module network for robust vehicle detection in low-light conditions

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Abstract

Vehicle detection under low-light conditions remains a significant challenge in the field of computer vision, exerting a notable impact on crucial applications such as autonomous driving and surveillance systems. Although the existing deep learning-based detection methods have achieved remarkable success under normal lighting conditions, their performance degrades significantly in low-light environments due to issues like insufficient brightness, low contrast, and loss of detailed features. This paper presents LLD-YOLO, an enhanced YOLOv11 for low-light vehicle detection. It incorporates improvements from a DarkNet module adapted from Self-Calibrating Illumination Learning for enhancing low-light images via adaptive illumination adjustment, a C3k2-RA feature extraction enhancement module that combines convolutional operations with self-attention mechanisms to overcome local receptive field limitations and capture global contextual information, and a Con-AM feature fusion module that optimizes multi-scale feature integration through an attention mechanism for adaptive feature selection and enhancement. Extensive experiments on Exdark demonstrate that our proposed LLD-YOLO achieves superior detection performance compared to existing methods, with significant improvements in detection accuracy and robustness under various low-light conditions. The mean average precision (mAP) of our method reaches 83.3%, which is a 4.5% improvement over the baseline model, while maintaining efficient computational performance.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

This work was supported in part by the National Key R&D Program of China under Grant Number 2022YFB2602200, in part by the National Natural Science Foundation of China under Grant 62273263, Grant 72171172, Grant 71771176 and Grant 92367101, in part by the Aeronautical Science Foundation of China under Grant 2023Z066038001, in part by the National Natural Science Foundation of China Basic Science Research Center Program under Grant 62088101, in part by Municipal Science and Technology Major Project under Grant 2022-5-YB-09, in part by the Natural Science Foundation of Shanghai under Grant 23ZR1465400, and in part by the Fujian Province’s Education and Scientific Research Projects for Young and Middle-aged Teachers in 2022 under Grant JAT220652.

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Authors and Affiliations

  1. Department of Information Engineering, Fuzhou Polytechnic, Lianrong Road, Fuzhou, 350108, Fujian, China

    Qin Zhang

  2. Sino-German College of Applied Sciences, Tongji University, Cao’an Highway, Shanghai, 201804, Shanghai, China

    Weian Guo

  3. Department of Traffic Engineering, Fuzhou Polytechnic, Lianrong Road, Fuzhou, 350108, Fujian, China

    Meibin Lin

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  1. Qin Zhang
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  2. Weian Guo
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Qin Zhang wrote the entire manuscript text. The second author reviewed the manuscript and provided valuable suggestions for revisions. The third author also reviewed the manuscript. All authors have participated in the review process of the manuscript to ensure its quality and integrity.

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Correspondence to Qin Zhang.

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Zhang, Q., Guo, W. & Lin, M. LLD-YOLO: a multi-module network for robust vehicle detection in low-light conditions. SIViP 19, 271 (2025). https://doi.org/10.1007/s11760-025-03858-6

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