计算机工程与应用2025，Vol.61Issue(6)：106-117,12.DOI:10.3778/j.issn.1002-8331.2410-0127
基于CDD-YOLO的轻量级低光照目标检测算法
Lightweight Low-Light Object Detection Algorithm Based on CDD-YOLO
摘要
Abstract
To address the challenges of low detection accuracy,high computational costs,and excessive memory con-sumption 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.关键词
低照度/YOLOv8/注意力机制/损失函数/轻量化网络Key words
low-light/YOLOv8/attention mechanism/loss function/lightweight network分类
计算机与自动化引用本文复制引用
史丽晨,杨超,刘雪超,周星宇..基于CDD-YOLO的轻量级低光照目标检测算法[J].计算机工程与应用,2025,61(6):106-117,12.基金项目
陕西省重点研发项目(2023-YBGY-386). （2023-YBGY-386）
