关键词: 低照度, YOLOv8, 注意力机制, 损失函数, 轻量化网络

Abstract: To address the challenges of low detection accuracy, high computational costs, and excessive memory consumption encountered by target detection algorithms in low-light conditions, this paper proposes a lightweight low-light target detection network model, CDD-YOLO, to enhance the performance of YOLOv8. Firstly, a multi-scale convolutional module based on a coordinate attention mechanism is proposed to extract texture features from different sensory fields and to capture long-range dependencies between spatial locations. Secondly, a dynamic head frame is integrated into the detection head to minimize the interference caused by complex backgrounds and scale variations. The bounding box regression loss function is designed using a dynamic non-monotonic focusing mechanism to enhance the regression path and quality of the anchor boxes, thereby improving the adaptability of model to variations in lighting and noise. Finally, redundant parameters in the model are pruned using a pruning algorithm to achieve model lightweighting. The self-constructed dataset, ExDark, and the VOC dataset are used for experimental validation. The experimental results show that the proposed method has better detection effect compared with the mainstream algorithms, and achieves a better balance between computational complexity and detection accuracy.

Key words: low-light, YOLOv8, attention mechanism, loss function, lightweight network