文献类型：期刊文献

英文题名：KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5

作者：Yang, Rujin[1];Li, Wenfa[1,2];Shang, Xinna[1,3];Zhu, Deping[1];Man, Xunyu[1]

第一作者：Yang, Rujin

通讯作者：Li, WF[1];Shang, XN[1];Li, WF[2];Shang, XN[3]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China;[3]Beijing Union Univ, Coll Robot, Beijing 100101, 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;[2]corresponding author), Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China;[3]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2023

卷号：12

期号：4

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85148870856);WOS:【SCI-EXPANDED(收录号:WOS:000938595900001)】；

基金：This research was funded by the National Natural Science Foundation of China (Grant Nos. 61972040).

语种：英文

外文关键词：small target detection; YOLOv5; K-means+; scSE attention module

摘要：At present, the existing methods have many limitations in small target detection, such as low accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE-YOLOv5 algorithm aiming to improve the ability of small target detection. The algorithm has three improvements based on the YOLOv5 algorithm. Firstly, it achieves more accurate size of anchor-boxes for small targets by K-means++ clustering technology. Secondly, the scSE (spatial and channel compression and excitation) attention module is integrated into the new algorithm to encourage the backbone network to pay greater attention to the feature information of small targets. Finally, the capability of small target feature extraction is improved by increasing the small target detection layer, which also increases the detection accuracy of small targets. We evaluate KPE-YOLOv5 on the VisDrone-2020 dataset and compare performance with YOLOv5. The results show that KPE-YOLOv5 improves the detection mAP by 5.3% and increases the P by 7%. The KPE-YOLOv5 algorithm has better detection outcome than YOLOv5 for small target detection.