计算机工程与应用2024，Vol.60Issue(13)：209-218,10.DOI:10.3778/j.issn.1002-8331.2401-0474
改进YOLOv8的航拍小目标检测方法:CRP-YOLO
Improved YOLOv8 Aerial Small Target Detection Method:CRP-YOLO
摘要
Abstract
UAV aerial target detection is a research hotspot in recent years.Due to the serious occlusion of small target images in the perspective of UAV aerial photography,problems such as missed detection and false detection occur.Aiming at the above problems,an improved YOLOv8 small target detection method in aerial photography is proposed:CRP-YOLO.Firstly,in order to improve the feature extraction ability of the neck network PANet,a multi-branch partial atrous convolu-tion structure is proposed.The RFB module is combined with PConv to improve the feature fusion method of the neck net-work and increase the receptive field of the neck network.Secondly,the contextual Transformer(CoT)structure is intro-duced into C2f before the SPPF layer of the backbone network to improve the Bottleneck block,and the global context information is used to improve the feature extraction ability of the network.Finally,a small target detection head with the size of 160×160 is added to the detection layer to improve the detection ability of small targets.Experiments are carried out on the public dataset VisDrone2019.The results show that compared with the baseline model YOLOv8s,CRP-YOLO increases by 3.8 percentage points on mAP@0.5,1.7 percentage points on mAP@0.5:0.95,and reduces the number of parame-ters by 1.5 MB.Compared with other mainstream target detection methods,it also obtains better detection performance.关键词
小目标检测/YOLOv8s/感受野模块(RFB)/CoTKey words
small target detection/YOLOv8s/receptive field block(RFB)/contextual Transformer(CoT)分类
信息技术与安全科学引用本文复制引用
赵志宏,郝子晔..改进YOLOv8的航拍小目标检测方法:CRP-YOLO[J].计算机工程与应用,2024,60(13):209-218,10.基金项目
国家自然科学基金(11972236). （11972236）
