上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 10: 1568 - 1580

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.578

电子信息与电气工程

随机平视摄像条件下的路边车辆违停检测

  • 詹泽辉 ,
  • 钟铭恩 ,
  • 袁彬淦 ,
  • 谭佳威 ,
  • 范康
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  • 1 厦门理工学院 福建省客车先进设计与制造重点实验室, 福建 厦门 361024
    2 厦门大学 航空航天学院, 福建 厦门 361005
詹泽辉(1997—),硕士生,从事智能交通和图像处理研究.
钟铭恩,教授;E-mail:zhongmingen@xmut.edu.cn.

收稿日期: 2023-11-14

  修回日期: 2024-01-04

  录用日期: 2024-01-17

  网络出版日期: 2024-02-09

基金资助

福建省自然科学基金资助项目(2023J011439);福建省自然科学基金资助项目(2019J01859)

收起

Detection of Roadside Vehicle Parking Violations Under Random Horizontal Camera Condition

  • ZHAN Zehui ,
  • ZHONG Ming’en ,
  • YUAN Bingan ,
  • TAN Jiawei ,
  • FAN Kang
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  • 1 Fujian Key Laboratory of Advanced Design and Manufacturing of Buses, Xiamen University of Technology, Xiamen 361024, Fujian, China
    2 School of Aerospace Engineering, Xiamen University, Xiamen 361005, Fujian, China

Received date: 2023-11-14

  Revised date: 2024-01-04

  Accepted date: 2024-01-17

  Online published: 2024-02-09

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摘要

查处车辆违停是城市交通管理的重要内容.鉴于人工执法耗时耗力、定点监控抓拍覆盖范围有限等问题,探索更为灵活高效的自动检测方法具有现实意义.提出一种适用于路面移动载体的非停留式、一次完成的巡航检测技术.在平视且随机拍摄角度条件下采集并构建车辆违停图像数据集XMUT-VPI,为研究提供数据基础.通过构建多任务神经网络(MTPN)作为编码器,提取违停判断所需的关键要素信息;借助自主设计的可变形大核特征聚合模块(DLKA-C2f)和跨任务交互注意力机制(CTIAM),实现了90.3%的最高目标平均检测准确率、4.4%的最小轮胎触地点平均定位误差,以及78.5%的次优车位线分割平均交并比精度.设计高效解码器来进一步提取车位线骨架特征,拟合主车位可视区域,匹配目标车辆,解析轮胎触地点与车位的位置关系,进而实现对违法停车、不当停车和规范停车3类典型行为的判定.实验结果表明,在各类复杂干扰情况下,该算法的综合准确率达到98.1%,领先现有主流方法,可为违停的全自动路面巡航治理提供技术支持.

关键词： 深度学习; 车辆违停; 目标检测; 关键点定位; 语义分割

本文引用格式

詹泽辉 , 钟铭恩 , 袁彬淦 , 谭佳威 , 范康 . 随机平视摄像条件下的路边车辆违停检测[J]. 上海交通大学学报, 2025 , 59(10) : 1568 -1580 . DOI: 10.16183/j.cnki.jsjtu.2023.578

Abstract

Investigation and punishment of vehicle parking violations is important in urban traffic management. Considering the time-consuming and labor-intensive nature of manual law enforcement, as well as the limited scope of fixed camera monitoring and detecting, exploring more flexible and efficient automatic detection methods has a great practical significance. Thus, a cruise detection technology is proposed, which is suitable for mobile carriers requiring no stopping and can be completed in a single pass. First, a vehicle parking violation image dataset named XMUT-VPI is collected and constructed under the conditions of approximate horizontal views and random shooting angles, laying a data foundation for the research. Then, a multitask parking network (MTPN) is constructed as an encoder to extract the key element information required for stop violation judgment. With the aid of the self-designed deformable large kernel feature aggregation module (DLKA-C2f) and cross-task interaction attention mechanism (CTIAM), a highest average detection accuracy of 90.3%, a minimum average positioning error of 4.4%, and a suboptimal average segmentation intersection ratio accuracy of 78.5% are achieved. Finally, an efficient decoder is designed to further extract the skeleton features of the parking space line and fit the visible area of the main parking space, which helps match the target vehicle and analyzes the positional condition between its tire ground-touching points and the main parking space. In addition, a judgment principle is provided for three typical behaviors of illegal parking, improper parking, and standardized parking. Experimental results show that the algorithm attains a comprehensive accuracy rate of 98.1% for vehicle parking violation detections across diverse complex interference scenarios, which outperforms existing mainstream methods and can provide technical supports for fully automate road cruise management of parking violatic.

Key words： deep learning; vehicle parking violation; object detection; key point localization; semantic segmentation

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