Vehicle Detection and Classification for Toll collection using YOLOv11 and Ensemble OCR

TL;DR

This paper introduces a cost-effective, high-accuracy toll collection system using YOLOv11 and ensemble OCR with a single camera, achieving high precision in vehicle and license plate detection under diverse conditions.

Contribution

It presents a novel, resource-efficient toll collection approach combining YOLOv11 and ensemble OCR, reducing hardware needs while maintaining high accuracy.

Findings

Mean Average Precision of 0.895 across conditions

98.5% license plate recognition accuracy

94.2% axle detection accuracy

Abstract

Traditional automated toll collection systems depend on complex hardware configurations, that require huge investments in installation and maintenance. This research paper presents an innovative approach to revolutionize automated toll collection by using a single camera per plaza with the YOLOv11 computer vision architecture combined with an ensemble OCR technique. Our system has achieved a Mean Average Precision (mAP) of 0.895 over a wide range of conditions, demonstrating 98.5% accuracy in license plate recognition, 94.2% accuracy in axle detection, and 99.7% OCR confidence scoring. The architecture incorporates intelligent vehicle tracking across IOU regions, automatic axle counting by way of spatial wheel detection patterns, and real-time monitoring through an extended dashboard interface. Extensive training using 2,500 images under various environmental conditions, our solution shows improved performance while drastically reducing hardware resources compared to conventional systems. This research contributes toward intelligent transportation systems by introducing a scalable, precision-centric solution that improves operational efficiency and user experience in modern toll collections.