Vehicle Violations Detection Using Deep Learning

Abstract

Overweight and over-dimension commercial vehicles accelerate pavement deterioration, increase crash severity, and impose significant operational burden on enforcement agencies. However, conventional enforcement approaches—static weighbridges and manual roadside inspections—are disruptive to traffic flow, labor-intensive, and difficult to scale for continuous monitoring on high-volume highways. This dissertation presents a Hybrid Vision/Virtual Weigh-in-Motion (V-WIM) framework that integrates certified Weigh-in-Motion (WIM) measurements with computer vision and deep learning to support automated, enforcement-oriented truck violation detection at highway speeds. The proposed pipeline comprises three key components. First, a YOLO/CNN perception stack detects commercial vehicles and axles, producing per-frame observations that include vehicle localization and measurement cues. Second, a Gated Recurrent Unit (GRU) temporal smoothing module stabilizes noisy frame-level dimension estimates to yield consistent per-vehicle length/width/height outputs suitable for compliance assessment. Third, a Graph Neural Network (GNN) fusion and association layer constructs a consistency graph over candidate WIM records and vision tracklets and performs graph-based inference to associate cross-modal observations and produce a final violation score and decision under legal thresholds. A standardized data orchestration layer outputs synchronized event records in structured formats (e.g., CSV/YAML), enabling repeatable training, evaluation, and auditing of results. The framework is evaluated across detection, association, and decision stages using precision, recall, F1-score, AP, ROC-AUC, confusion matrices, and threshold sweeps. Results indicate that combining temporal smoothing with graph-based fusion improves measurement stability and decision reliability compared with a YOLO-only baseline, supporting scalable, real-time monitoring and more defensible violation decisions for intelligent transportation enforcement. Vehicle Violations Detection.