CEM-YOLO: multi-branch residual feature fusion and convolutional maxpooling downsampling for real-time vehicle detection in night scenarios
Real-time vehicle detection at night faces significant challenges: ineffective low-light feature extraction, low computational efficiency hampering real-time performance, subpar detection of small and occluded vehicles, and limited cross-scenario generalization. To address these issues, this paper p...
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Published in | Signal, image and video processing Vol. 19; no. 9 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.09.2025
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1863-1703 1863-1711 |
DOI | 10.1007/s11760-025-04313-2 |
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Summary: | Real-time vehicle detection at night faces significant challenges: ineffective low-light feature extraction, low computational efficiency hampering real-time performance, subpar detection of small and occluded vehicles, and limited cross-scenario generalization. To address these issues, this paper presents CEM-YOLO, an enhanced YOLO algorithm leveraging deep learning for nighttime vehicle detection. Two novel modules, Convolutional Maxpooling Downsampling and Multi-branch Residual Feature Fusion, are introduced to mitigate model complexity, reduce feature redundancy, and safeguard input features. Additionally, the Efficient Multi-Scale Attention Module is integrated into the Neck network’s detection layers. Extensive experiments and ablation studies on benchmark datasets demonstrate that CEM-YOLO excels in nighttime scenarios, achieving an optimal speed-accuracy balance for real-time applications. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 1863-1703 1863-1711 |
DOI: | 10.1007/s11760-025-04313-2 |