Abstract
Detecting vehicles under low illumination conditions poses a significant challenge due to reduced visibility and lack of contrast. To address this issue, this paper proposes a Low Illumination Vehicle Detection Network (LIVDN). LIVDN utilizes the Dilation-Wise Residual module to enhance the feature extraction network, allowing for a more comprehensive capture of contextual information. The Bidirectional Cascade Feature Fusion module improves detection capabilities for vehicles of various sizes. Additionally, a Bi-level Routing Spatial Attention module directs the network’s attention to vehicle texture features and color information, enhancing detection accuracy. The proposed method is validated on the BDD100K dataset and KITTI dataset. Experimental results demonstrate a significant improvement in vehicle detection accuracy under low illumination conditions.
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Funding
This research has been supported by Science and Technology Development Plan Project of Jilin Province, China (Grant No. 20240304145SF). The authors also want to thank Changchun Computing Center for providing inclusive computing power and technical support during the completion of this paper.
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Lan Liu and Fei Yan conceived and designed the study;Yuzhuo Shen and Siyu Li were responsible for collecting and analyzing the data;Yunqing Liu provided guidance and oversight throughout. All authors contributed to the article and approved the submitted version.
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Liu, L., Yan, F., Shen, Y. et al. LIVDN: low illumination vehicle detection network. SIViP 19, 44 (2025). https://doi.org/10.1007/s11760-024-03635-x
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DOI: https://doi.org/10.1007/s11760-024-03635-x