计算机应用与软件2025，Vol.42Issue(5)：164-170,190,8.DOI:10.3969/j.issn.1000-386x.2025.05.023
基于改进YOLOv4-Tiny的交通标志图像识别算法研究
TRAFFIC SIGN IMAGE RECOGNITION ALGORITHM BASED ON IMPROVED YOLOV4-TINY
摘要
Abstract
In order to realize the accurate recognition of traffic signs by autonomous vehicle,a traffic sign image recognition algorithm YOLO-slim based on improved YOLOv4-Tiny is proposed.Convolution attention module was added to the original network and shallow features were introduced into the feature pyramid network to improve the utilization rate of feature information between different layers.Depthwise separable convolution was used to replace standard convolution to reduce the number of network parameters and compress the model weight file.Focus loss function was used to balance difficult samples in model training.Experimental results show that YOLO-slim's mean average precision is 94.41％,weight file is 4.49 MB,and detection speed is 8.0 ms.The improved algorithm has higher accuracy and smaller weight files,and is more suitable for deployment in vehicle-mounted computing units.关键词
交通标志/算法/注意力机制/深度可分离卷积Key words
Traffic signs/Algorithm/Attention mechanism/Depthwise separable convolution分类
计算机与自动化引用本文复制引用
孙海明,付世超..基于改进YOLOv4-Tiny的交通标志图像识别算法研究[J].计算机应用与软件,2025,42(5):164-170,190,8.基金项目
湖北省科技厅重点项目(2021BED004). （2021BED004）
