计算机与现代化Issue(10)：7-13,7.DOI:10.3969/j.issn.1006-2475.2025.10.002
融合空间信息的YOLOv7交通标志检测
Fusion of Spatial Information for YOLOv7 Traffic Sign Detection
摘要
Abstract
During the detection process of traffic signs,due to the influence of weather and light intensity,problems such as false detections and missed detections occur during detection.To solve this problem,a traffic sign detection algorithm combining spa-tial information is proposed.Firstly,coordinate convolution is used on network to enhance sensitivity of the network to coordinate position information.Additionally,the incorporation of a coordinate attention mechanism into the backbone features enables better focus on spatial location information at fusion points.Moreover,the feature fusion process utilizes a multi-scale weighted network and pyramid pooling,leveraging weighted calculations and skip connections to enhance semantic information fusion between low-level and high-level layers.Lastly,the adoption of the SIoU loss function enhances target positioning accuracy.The experimental results on the CCTSDB2021 and GTSDB datasets demonstrate that this method achieved mean Average Precision(mAP)values of 84.9%and 98.5%respectively.Compared with mainstream detection models,it shows significant improvement—exceeding the original model by 5.39 percentage points and 1.67 percentage points—thus enhancing the detection accuracy of traffic signs.关键词
交通标志检测/坐标卷积/注意力机制/多尺度融合/SIoU损失函数Key words
traffic sign detection/coordinate convolution/attention mechanism/multi-scale fusion/SIoU loss function分类
信息技术与安全科学引用本文复制引用
师红宇,张哲于,杜文,李怡..融合空间信息的YOLOv7交通标志检测[J].计算机与现代化,2025,(10):7-13,7.基金项目
陕西省重点研发计划项目(2022GY-058,2022GY-074) （2022GY-058,2022GY-074）
