Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram

XIONG Qunfang; LIN Jun; YUAN Xiwen; XU Yanghan; YUE Wei; LI Yuanzhengyu

doi:10.13890/j.issn.1000-128X.2024.01.244

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Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram

Intelligent Technology | 更新时间：2025-02-08

- Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram
- Electric Drive for Locomotives Issue 6, Pages: 140-148(2024)
- 作者机构：
  
  中车株洲电力机车研究所有限公司，湖南株洲 412001
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2024.01.244
  CLC： U239.5;TP391
- Published：10 November 2024，
  
  Received：10 July 2023，
  
  Revised：15 April 2024，
- 稿件说明：
移动端阅览
熊群芳, 林军, 袁希文, 等. 基于深度学习的智轨信号灯检测和识别方法[J]. 机车电传动, 2024(6): 140-148.

XIONG Qunfang, LIN Jun, YUAN Xiwen, et al. Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram[J]. Electric drive for locomotives,2024(6): 140-148.
熊群芳, 林军, 袁希文, 等. 基于深度学习的智轨信号灯检测和识别方法[J]. 机车电传动, 2024(6): 140-148. DOI：10.13890/j.issn.1000-128X.2024.01.244.

XIONG Qunfang, LIN Jun, YUAN Xiwen, et al. Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram[J]. Electric drive for locomotives,2024(6): 140-148. DOI：10.13890/j.issn.1000-128X.2024.01.244.

摘要

智轨电车作为中车株洲电力机车研究所有限公司自主研发的智能轨道快运系统（简称“智轨”），其交通信号灯的检测与识别是提升智轨自动驾驶系统安全性的关键技术。智轨交通信号灯除了少部分通用信号灯，绝大多数为定制信号灯，而目前已有的信号灯检测与识别方法无法满足智轨自动驾驶环境下的检测要求。因此，文章利用深度学习算法对智轨信号灯检测与识别开展了相关研究工作，首先，通过高精度地图信息确定信号灯的RoI( Region of Interest )区域，缩小对智轨信号灯检测的范围，提升检测速度；其次，采用改进的YOLOV5s网络对RoI区域进行特征提取，检测出智轨交通信号灯；最后，对提取的交通信号灯图片采用MobileNetV2轻量级网络进行识别分类，确定信号灯的具体类别。为进一步增强模型的泛化性能，在信号灯检测之前增加了图像诊断算法，针对曝光、逆光等复杂环境及时提示预警，同时保存这些非正常数据，用于信号灯的检测与分类模型训练，进一步优化模型。试验结果表明，文章提出的方法对于智轨信号灯的检测与识别取得较好效果，白天在指定道路上平均精度均值达到84.76%，且实时性能良好。

Abstract

The autonomous rail rapid transit (ART) system is an intelligent express transport solution developed independently by CRRC Zhuzhou institute co.

ltd. The detection and recognition of traffic lights are critical for improving the safety of the ART automatic operation system. However

existing means for traffic light detection and recognition often fall short of meeting the detection requirements in the automatic operation environments characterized by customized traffic lights

with only a few regular signals. This paper presents study efforts in this field through the application of a deep learning algorithm. Firstly

regions of interest (RoIs) for traffic lights were determined using high-precision map information to narrow the detection range and improve the detection speed. Secondly

an improved YOLOV5s network was employed to extract features from the RoIs

facilitating the recognition of ART traffic lights. Finally

the extracted traffic signal images were classified using a MobileNetV2 lightweight network to identify specific signal categories. In order to further enhance the model's generalization performance

an image diagnosis algorithm was introduced before signal recognition

which generates warnings for complex conditions

such as overexposure and backlighting. These abnormal image data were saved and utilized for further training and optimizing the model. Experimental results reveals that the proposed approach effectively detected and recognized ART traffic lights

achieving an average detection precision of 84.76% on designated roads during the daytime while exhibiting good real-time performance.

关键词

深度学习智轨交通信号灯高精度地图YOLOV5sMobileNetV2图像诊断

Keywords

deep learningART traffic lighthigh-precision mapYOLOV5sMobileNetV2image diagnosis

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