计算机工程与应用2024，Vol.60Issue(9)：135-141,7.DOI:10.3778/j.issn.1002-8331.2310-0131
结合Transformer和动态特征融合的低照度目标检测
Low-Light Object Detection Combining Transformer and Dynamic Feature Fusion
摘要
Abstract
To address the issues of high parameter and computational complexity,poor real-time performance,and limited applicability to mobile devices in existing low-light object detection algorithms,this paper proposes an improved light-weight model called DarkYOLOv8 based on YOLOv8 for low-light object detection.Firstly,MobileNet v2 is replaced the backbone network of YOLOv8 to enhance the feature extraction capabilities of the model.Secondly,the Transformer attention mechanism is utilized to capture global information from the images and the Transformer module parameters are trained based on target annotation information as labels to enhance the weights within the target regions,thereby improving the capability of the model to extract target features under low-light conditions.Finally,the dynamic feature fusion attention(DFFA)module is employed for feature fusion in the neck network,dynamically fusing shallow and deep features,simul-taneously,the YOLOv8X algorithm is employed in combination with CBAM to supervise the training of spatial attention weights in the CBAM module of DFFA.The experimental results show that on the ExDark dataset,DarkYOLOv8 achieves 70.1%on the mAP50 metric with only 8.53 GFLOPs,which is a 3.9 percentage points improvement compared to YOLOv8n.关键词
低照度目标检测/注意力机制/轻量化/Transformer/可变形卷积Key words
low-light object detection/attention mechanism/lightweight/Transformer/deformable convolution分类
信息技术与安全科学引用本文复制引用
蔡腾,陈慈发,董方敏..结合Transformer和动态特征融合的低照度目标检测[J].计算机工程与应用,2024,60(9):135-141,7.基金项目
国家自然科学基金新疆联合基金重点项目(U1703261). （U1703261）
