AI-Based Intelligent HSRP Compliance Screening and Decision Support System Using Computer Vision

HSRPs are designed to make it uniform and to facilitate quick identification of vehicles but the surveillance of vast amounts of traffic manually is expensive and unfeasible. In this report, an AI-powered HSRP compliance screening and decision-support system is proposed that focuses on traffic-video screening and is based on computer vision. The infrastructure uses a YOLO-based traffic video analysis module for vehicle and HSRP detection, and Byte Track for vehicle tracking to associate vehicles across frames. Vehicle detections are later improved through best-frame selection, and multiple observations are filtered to accumulate evidence on each vehicle before OCR recognition. PaddleOCR is selected as the main OCR engine, and OCR results are fused across frames using temporal evidence while considering aspect ratio and registration-format constraints to reduce the impact of misclassification. The visible evidence of HSRPs, including IND-band evidence, plate quality, aspect ratio, and OCR confidence, is combined into a screening assessment. Tracking, OCR, HSRP screening, explainable prediction and reasoning, and human image-rejection and failure mode checks are combined within a streamlined vehiclescreening tool, distinguishing vehicles that are likely to be compliant from those that are potentially subject to further human review. All plate and video evidence, and screening information, reasoning, and failure modes are presented via an API-powered display available for download and human review. The entire system is validated through end-to-end traffic video analysis and automated testing, though it does not seek to achieve comprehensive legal authentication of HSRP security features.