Lane-Based Vehicle Counting System for Complex Traffic Scenes

Le résumé fourni par la source

To address the reliance on manual calibration and the performance degradation caused by loosely coupled modules in lane-wise vehicle counting under complex traffic scenarios, this paper presents a lane-wise vehicle counting system based on adaptive lane partitioning and multi-module integration. The system first applies YOLOPv2 for lane-line detection, providing the basis for lane-region partitioning. Subsequently, Hue-Saturation-Value (HSV) color segmentation, morphological processing, and contour filtering are employed to enhance the robustness of lane feature extraction. Leveraging perspective geometry, lane regions are constructed to achieve adaptive lane partitioning. For vehicle analysis, YOLOv11 is utilized for vehicle detection, and ByteTrack is adopted for multi-object tracking. These modules are combined with lane assignment to form an integrated pipeline that preserves trajectory continuity and mitigates identity loss under occlusion and motion blur. Furthermore, a PyQt5-based interactive visualization interface is developed to support video processing, real-time display, lane-region visualization, and statistical analysis of per-lane traffic flow and lane-change behaviors. Experimental results demonstrate the effectiveness and practicality of the proposed system in complex multi-lane traffic scenarios.

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