计算机工程与应用2025，Vol.61Issue(21)：129-143,15.DOI:10.3778/j.issn.1002-8331.2504-0038
PATD-YOLO:基于YOLOv11的道路障碍目标检测算法
PATD-YOLO:Road Obstacle Object Detection Algorithm Based on YOLOv11
纪文杰 1宋廷伦 2容小洛 1周迈1
作者信息
- 1. 南京航空航天大学 能源与动力学院,南京 210016
- 2. 奇瑞汽车股份有限公司,安徽 芜湖 241007
- 折叠
摘要
Abstract
Road obstacle detection is crucial for ensuring driving stability and traffic safety.To address the issues of false detection,missed detection,and insufficient accuracy in small object detection caused by complex road textures and diverse obstacles,this paper proposes an improved road obstacle detection model based on YOLOv11,named PATD-YOLO.A dual-backbone network is designed,in which the multi-convolution pooling stem(MCPStem)module shares shallow features,while HGNetV2 is introduced as the second backbone to enhance parameter representation capability through dynamic convolution.The feature dynamic aligning merge(FDAM)module eliminates spatial discrepancies in multi-source features,enabling efficient feature fusion.A small-object-enhanced feature pyramid is constructed by incor-porating high-resolution P2-layer feature maps.SPDConv operations are employed to preserve small object information,and the global-local aggregate feature information(GLAFI)module is designed to integrate fine-grained shallow features with deep semantic features.A reparameterized lightweight detection head is introduced using reparameterizable convolu-tion to improve parameter efficiency.Experimental results demonstrate that,with nearly identical parameters and computa-tional costs,the proposed algorithm achieves 3.0 and 4.3 percentage points improvements in mAP@0.5 on a self-built dataset and the public RDD2022 dataset,respectively,outperforming mainstream algorithms.Moreover,the detection speed reaches 131.6 FPS,meeting real-time requirements for vehicle detection.关键词
道路障碍检测/YOLOv11/特征动态融合/可重参数化Key words
road obstacle detection/YOLOv11/feature dynamic fusion/reparameterizable分类
信息技术与安全科学引用本文复制引用
纪文杰,宋廷伦,容小洛,周迈..PATD-YOLO:基于YOLOv11的道路障碍目标检测算法[J].计算机工程与应用,2025,61(21):129-143,15.
