计算机应用与软件2024，Vol.41Issue(8)：282-288,7.DOI:10.3969/j.issn.1000-386x.2024.08.041
面向无人驾驶场景下的道路多目标检测算法
A MULTI-TARGET DETECTION ALGORITHM OF ROAD FOR UNMANNED DRIVING SCENE
摘要
Abstract
Aimed at the problem of high false detection rates of object detection in unmanned driving scene,a multi-target detection algorithm with improved YOLOv3 is proposed.The groups convolution kernel was introduced into the original feature network Darknet53 to replace the original convolution kernel,which reduced the complexity of convolution operation.The original feature fusion was improved to make the fusion of different scales more fully,and it improved the detection effect of occluded and small targets.The CIoU loss function was constructed to make the network convergence better.Experimental results show that the average accuracy of the improved YOLOv3 algorithm is increased by 1.71％,and the false detection rate is reduced by 12％,which is significantly better than the YOLOv3 algorithm.关键词
无人驾驶/多目标检测/分组卷积/YOLOv3/CIoU损失函数Key words
Driverless/Multi-target detection/Group convolution/YOLOv3/CIoU loss function分类
信息技术与安全科学引用本文复制引用
牛文杰,伊力哈木·亚尔买买提..面向无人驾驶场景下的道路多目标检测算法[J].计算机应用与软件,2024,41(8):282-288,7.基金项目
国家自然科学基金项目(61866037,61462082). （61866037,61462082）
