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 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.