文献类型：期刊文献

英文题名：Research on Improved YOLOv5 for Low-Light Environment Object Detection

作者：Wang, Jing[1];Yang, Peng[1];Liu, Yuansheng[1];Shang, Duo[2];Hui, Xin[2];Song, Jinhong[3];Chen, Xuehui[3]

通讯作者：Yang, P[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]China Acad Ind Internet, Beijing 100102, Peoples R China;[3]Jiaojia Gold Mine Shandong Gold Min Co Ltd, Laizhou 261441, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

年份：2023

卷号：12

期号：14

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85166257748);WOS:【SCI-EXPANDED(收录号:WOS:001035133200001)】；

语种：英文

外文关键词：low-light scenarios; object detection; image enhancement; YOLOv5; underground mine scenarios

摘要：Object detection in low-light scenarios has been widely acknowledged as a significant research area in the field of computer vision, presenting a challenging task. Aiming at the low detection accuracy of mainstream single-stage object detection models in low-light scenarios, this paper proposes a detection model called DK_YOLOv5 based on YOLOv5, specifically designed for such scenarios. First, a low-light image enhancement algorithm with better results is selected to generate enhanced images that achieve relatively better visual effects and amplify target features. Second, the SPPF layer is improved to an R-SPPF module with faster inference speed and stronger feature expression ability. Next, we replace the C3 module with the C2f module and incorporate an attention mechanism to develop the C2f_SKA module, enabling richer gradient information flow and reducing the impact of noise features. Finally, the model detection head is replaced with a decoupled head suitable for the object detection task in this scenario to improve model performance. Additionally, we expand the Exdark dataset to include low-light data of underground mine scenario targets, named Mine_Exdark. Experimental results demonstrate that the proposed DK_YOLOv5 model achieves higher detection accuracy than other models in low-light scenarios, with an mAP0.5 of 71.9% on the Mine_Exdark dataset, which is 4.4% higher than that of YOLOv5.