Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

CEM-YOLO: multi-branch residual feature fusion and convolutional maxpooling downsampling for real-time vehicle detection in night scenarios

  • 期刊论文

  • 发表日期：

    2025年6月18日

查看全文
  • Li-Juan Liu,
  • Rushi Jia,
  • Hamid Reza Karimi

摘要

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.