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、定点监控抓拍作用域受限等问题，探索更为灵活高效的自动检测方法具有现实意义。为此提出一种适用于路面移动载体的无需停留且一次完成的巡航检测技术。首先在平视且随机拍摄角度条件下采集并构建了一个车辆违停图像数据集XMUT-VPI，为研究奠定数据基础。然后构建了一个多任务神经网络MTPN作为编码器来提取违停判断所需的关键要素信息，通过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，相比同时取得了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和78.5%的次优车位线分割平均交并比精度。最后设计了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、匹配目标车辆和解析轮胎触地点与车位的位置关系，给出违法停车、不当停车和规范停车三类典型行为的判定原理。实验表明，在各类复杂干扰情况下的综合准确率达到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。

Investigation and punishment of vehicle parking violations is an important part of urban traffic management. Considering of 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 great practical significance. Thus, a cruise detection technology suitable for road moving carriers that does not require stopping and can be completed in one go was proposed for this purpose. Firstly, a vehicle parking violation image dataset named XMUT-VPI was collected and constructed under the conditions of approximate horizontal views and random shooting angles, laying a data foundation for the research. Then, a multitask neural network MTPN was constructed to simultaneously carried out vehicle target detection, tire contact location, and parking line segmentation. By help of the self-designed deformable large kernel feature aggregation module and cross-task interaction attention mechanism, an highest average detection accuracy of 90.3%, an minimum average positioning error of 4.4%, and an suboptimal average segmentation intersection ratio accuracy of 78.5% were achieved. At last, based on the network inference results, skeleton as well as it’s features of the parking line were extracted and visible area of the main parking space was fitted. These help to match a target vehicle and analyze the positional condition between its tire groundtouching points and the main parking space. Based on all these works, a judgment algorithm was provided for three typical behaviors: illegal parking, improper parking, and standardized parking. Experimental results confirmed that the algorithm attains a comprehensive accuracy rate of 98.1% for vehicle parking violation detections across diverse complex interference scenarios, which leads existing mainstream methods and is able to provide technical supports for fully automatic road cruise management.