摘要
Abstract
On normally operating highways,there are dangerous targets that interfere with drivers'judgment and pose traffic hazards.When using drones for detection,there are challenges such as occlusion,overlap,dispersion and heterogeneity.To address these issues,a high-precision detection algorithm based on YOLOv8n,called CT-YOLO,is proposed.Firstly,dilated convolu-tion is reconstructed in the C2f module of the YOLOv8 backbone network,and 1×1 convolutions are integrated before and after the convolution to solve the problem of decentralized targeting of application scenarios.Secondly,the classic fea-ture pyramid network is improved,and two additional detection layers are added to enhance detection accuracy for occluded targets.Lastly,an improved triple attention mechanism is integrated into the Head part of the C2f module to enhance the model's ability to capture heterogeneous target information.An image dataset containing 11 types of anomalous targets,including fractures,patches,pericarp,leaves,plastic,potholes,arrows,lane lines,cardboard boxes,oil,and cans,is constructed through video collection,frame extraction,manual annotation,and data augmentation.Experimental results indicate that the CT-YOLO algorithm improves mAP@0.5 by 13.2 percentage points and mAP@0.5:0.95 by 11 percentage points on the anomalous target image dataset,significantly enhancing detection accuracy and demonstrating good practical application effectiveness.关键词
高速公路/无人机(UAV)/YOLOv8/目标检测/多目标/小目标Key words
highway/unmanned aerial vehicle(UAV)/YOLOv8/target detection/multiple targets/small targets分类
计算机与自动化
