计算机工程与应用2025，Vol.61Issue(11)：144-155,12.DOI:10.3778/j.issn.1002-8331.2409-0128
RO-YOLOv9车辆行人检测算法
RO-YOLOv9 Vehicle and Pedestrian Detection Algorithm
摘要
Abstract
Aiming at the low detection accuracy,false detection and missed detection problems caused by small or occluded vehicle and pedestrian targets in road traffic environments,a road target detection algorithm RO-YOLOv9 is proposed.Firstly,the small target detection layer is added to enhance the algorithm's feature learning ability for small targets.Sec-ondly,the bidirectional and adaptive scale fusion feature pyramid network(BiASF-FPN)structure is designed to optimize multi-scale feature fusion and ensure that the algorithm effectively captures detailed information from small to large scale targets.Thirdly,the OR-RepN4 module is proposed to simplify the complex algorithm structure and improve the inference speed through the re-parameterization strategy.Finally,the shape neighborhood weighted decomposition(Shape-NWD)loss function is used to focus on the shape and size of the bounding box,and the normalized Gaussian Wasserstein dis-tance smoothing regression is used to achieve cross-scale invariance and reduce the detection error of small-scale and occluded targets.The experimental results show that under the optimized SODA10M and BDD100K datasets,the mAP@0.5(mean average precision)of RO-YOLOv9 algorithm reaches 68.1%and 56.8%,respectively,which is 5.6 percentage points and 4.4 percentage points higher than that of the YLOLOv9 algorithm,and the detection frame rates reach 55.3 and 54.2 frames per second,respectively,achieving a balance between detection accuracy and detection speed.关键词
YOLOv9/小目标检测/双向与自适应尺度融合特征金字塔网络(BiASF-FPN)/OR-RepN4/Shape-NWDKey words
YOLOv9/small target detection/bidirectional and adaptive scale fusion feature pyramid network(BiASF-FPN)/OR-RepN4/shape neighborhood weighted decomposition(Shape-NWD)分类
信息技术与安全科学引用本文复制引用
廖炎华,万学俊,赵周洲,潘文林..RO-YOLOv9车辆行人检测算法[J].计算机工程与应用,2025,61(11):144-155,12.基金项目
国家自然科学基金(62362071). （62362071）
