基于多尺度特征提取与特征融合的交通标志检测

doi:10.19678/j.issn.1000-3428.0062282

计算机工程 ›› 2022, Vol. 48 ›› Issue (10): 270-278,287. doi: 10.19678/j.issn.1000-3428.0062282

基于多尺度特征提取与特征融合的交通标志检测

张永亮¹, 陆阳^1,2,3, 朱芜强¹, 卫星^1,3,4, 魏臻^1,2,3

1. 合肥工业大学计算机与信息学院, 合肥 230009;
2. 矿山物联网与安全监控技术安徽省重点实验室, 合肥 230088;
3. 安全关键工业测控技术教育部工程研究中心, 合肥 230009;
4. 合肥工业大学智能制造研究院, 合肥 230009

收稿日期:2021-08-07 修回日期:2021-11-03 发布日期:2021-11-09
作者简介:张永亮(1997—),男,硕士研究生,主研方向为交通图像处理、目标检测、深度学习;陆阳(通信作者),教授、博士;朱芜强,硕士研究生;卫星,副教授、博士;魏臻,教授、博士。
基金资助:
国家重点研发计划专项（2018YFC0604404）；中央高校基本科研业务费专项资金（PA2021GDGP0061）；安徽省重点研发计划项目（202004a05020040）；合肥工业大学智能制造技术研究院智能网联及新能源汽车专项（IMIWL2019003）。

Traffic Sign Detection Based on Multi-Scale Feature Extraction and Feature Fusion

ZHANG Yongliang¹, LU Yang^1,2,3, ZHU Wuqiang¹, WEI Xing^1,3,4, WEI Zhen^1,2,3

1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China;
2. Anhui Mine IoT and Security Monitoring Technology Key Laboratory, Hefei 230088, China;
3. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei 230009, China;
4. Intelligent Manufacturing Institute, Hefei University of Technology, Hefei 230009, China

Received:2021-08-07 Revised:2021-11-03 Published:2021-11-09

摘要/Abstract

摘要： 基于卷积神经网络的交通标志检测算法在对现实中复杂的交通场景图像进行交通标志检测时，难以同时解决定位和分类两项任务，并且目标检测领域相关算法所使用的公开数据集提供的图像和交通标志的种类不能满足现实交通场景中复杂的情况。建立一个新的道路交通标志数据集，在YOLOv4算法的基础上针对现实交通场景图像的复杂性和图像中交通标志尺寸差异较大的特点，设计多尺寸特征提取模块和增强特征融合模块，提高算法同时定位和分类交通标志的能力。在此基础上，对算法中不同的模块设置不同的参数进行对照实验，得到一组表现最优的参数，用于检测现实交通场景图片中的交通标志。在道路交通标志数据集上的实验结果表明，该算法相比基于卷积神经网络的同类型任务目标检测算法具有更高的检测精度，平均精度均值达到83.63%。

关键词: 交通标志检测, 自动驾驶, 卷积神经网络, 目标检测, 深度学习

Abstract: The traffic sign detection algorithm based on Convolutional Neural Networks (CNN) cannot easily solve localization and classification tasks simultaneously when detecting traffic signs in actual complex traffic scene images. Additionally, the images and types of traffic signs provided by the public dataset used in the related algorithms do not satisfy the situations in actual traffic.Hence, this paper presents a new dataset of road traffic signs. Subsequently, to address the complexity of actual traffic scene images and the significant variation in traffic sign sizes in the images, a multisize feature extraction module and an enhanced feature fusion module are designed based on the YOLOv4 algorithm to improve the algorithm's ability in locating and classifying traffic signs simultaneously.Different parameters are set for the different modules in the algorithm to perform comparative experiments, and a set of parameters with the best performance is obtained, which is then used to detect traffic signs in actual traffic scenes.Experimental results obtained based on the newly created dataset show that the improved algorithm achieves a mean average precision of 83.63%, which is higher than those achieved by several well-established object detection algorithms based on CNN for the same type of task.

Key words: traffic sign detection, autonomous driving, Convolutional Neural Networks(CNN), object detection, deep learning

中图分类号:

TP391.41

张永亮, 陆阳, 朱芜强, 卫星, 魏臻. 基于多尺度特征提取与特征融合的交通标志检测[J]. 计算机工程, 2022, 48(10): 270-278,287.

ZHANG Yongliang, LU Yang, ZHU Wuqiang, WEI Xing, WEI Zhen. Traffic Sign Detection Based on Multi-Scale Feature Extraction and Feature Fusion[J]. Computer Engineering, 2022, 48(10): 270-278,287.

http://www.ecice06.com/CN/Y2022/V48/I10/270

图/表 11

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