关键词: YOLOv8, 夜间车辆检测, 深度学习, 注意力机制, 小目标检测

Abstract: To address the decline in vehicle detection performance in nighttime environments, an improved algorithm based on YOLOv8 is proposed, referred to as YOLO-SA (You Only Look Once with Synergistic Attention). The algorithm incorporates Spatial and Channel Synergistic Attention (SCSA) into the backbone network of YOLOv8, constructing the C2f with Spatial and Channel Synergistic Attention (C2f-SCSA) module to enhance feature extraction capabilities for nighttime images. Next, the Four-Adaptively Spatial Feature Fusion (FASFF) detection head is used to optimize the algorithm's ability to process multi-scale features and improve its detection capability for small objects. Finally, the sample weighting function Slideloss is adopted to replace the original classification loss function, which improves the algorithm's ability to identify difficult samples and alleviates the problem of unbalance of difficult and easy samples. Experimental results demonstrate that on the BDD100K dataset, YOLO-SA achieves higher accuracy in the nighttime vehicle detection task, which outperforms YOLOv8 by 3.4 percentage points in mAP0.5 and 2.9 percentage points in mAP0.5:0.95. These improvements enable the model to more effectively address the challenges of vehicle detection in nighttime environments.

Key words: YOLOv8, nighttime vehicle detection, deep learning, attention mechanism, small target detection