改进YOLOv5的交通灯实时检测鲁棒算法  

Improved YOLOv5 Traffic Light Real-Time Detection Robust Algorithm

作　　者：钱伍 王国中 李国平 QIAN Wu;WANG Guozhong;LI Guoping(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《计算机科学与探索》2022年第1期231-241,共11页Journal of Frontiers of Computer Science and Technology

基　　金：国家重点研发计划(2019YFB1802700)。

摘　　要：交通灯检测算法作为自动驾驶任务中的一个重要环节,直接关系到智能汽车的行车安全。因为交通灯尺度小且环境复杂,给算法研究带来了困难。针对交通检测存在的痛点,提出改进YOLOv5的交通灯检测算法。首先使用可见标签比确定模型输入;然后引入ACBlock结构增加主干网络的特征提取能力,设计SoftPool减少主干网络的采样信息损失,使用DSConv卷积核减少模型参数;最后设计了记忆性特征融合网络,高效利用了高级语义信息和底层特征。对模型输入和主干网络的改进,直接提高模型在复杂环境下对特征的提取能力;对特征融合网络的改进,使模型能够充分利用特征信息,增加对目标定位和边界回归的精准度。实验结果表明,改进后的方法在BDD100K数据集上取得了74.3%的AP和111frame/s的检测速度,比YOLOv5提高11.0个百分点的AP;在Bosch数据集上取得了84.4%的AP和126frame/s的检测速度,比YOLOv5提高9.3个百分点的AP。鲁棒性测试结果表明,改进后的模型在各种复杂环境中对目标的检测能力都有显著提升,鲁棒性增加,做到了高精度实时检测。Traffic light detection algorithm,a critical procedure for realization of automatic driving,is directly related to the driving safety of intelligent vehicles.However,due to the small size of traffic lights and complicated environment,the algorithm research meets plenty of difficulties.This paper puts forward a traffic light detection algorithm based on optimized YOLOv5.Firstly,it uses a visible label ratio to determine the model input.Secondly,the ACBlock structure is introduced to increase the feature extraction ability of the backbone network;the SoftPool is designed to reduce the sample loss of the backbone network and the DSConv convolution kernel is used to reduce the model parameters.Finally,a memory feature fusion network is designed to efficiently utilize high level semantic information and low level features.As a result,the improvement of model input and backbone network directly improves the feature extraction ability of the model in complex environment;the improvement of feature fusion network enables the model to make full use of feature information and increase the accuracy of target positioning and boundary regression.Experimental results show that,the proposed algorithm achieves 74.3%AP and 111 frame/s detection speed on BDD100K,which is 11.0 percentage points higher than the AP of YOLOv5.In Bosch data set,84.4%AP and 126 frame/s detection speed are obtained,which is 9.3 percentage points higher than the AP of YOLOv5.The robustness test results show that the proposed algorithm has significantly improved the detection ability of targets in a variety of complex environments,and the robustness is increased to achieve high-precision real-time detection.

关 键 词：交通灯检测 YOLOv5 记忆性特征融合网络 BDD100K 实时检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]