VisionGuard: Synergistic Framework for Helmet Violation Detection

TL;DR

VisionGuard is a multi-stage framework that improves helmet violation detection accuracy by addressing environmental variability, class imbalance, and annotation inconsistencies through tracking-based refinement and contextual augmentation.

Contribution

It introduces a novel synergistic framework combining Adaptive Labeling and Contextual Expander modules to enhance detection reliability in challenging traffic surveillance scenarios.

Findings

Improves mAP by 3.1% over baseline detectors.

Enhances classification consistency via tracking-based label refinement.

Boosts recall for underrepresented classes with virtual bounding boxes.

Abstract

Enforcing helmet regulations among motorcyclists is essential for enhancing road safety and ensuring the effectiveness of traffic management systems. However, automatic detection of helmet violations faces significant challenges due to environmental variability, camera angles, and inconsistencies in the data. These factors hinder reliable detection of motorcycles and riders and disrupt consistent object classification. To address these challenges, we propose VisionGuard, a synergistic multi-stage framework designed to overcome the limitations of frame-wise detectors, especially in scenarios with class imbalance and inconsistent annotations. VisionGuard integrates two key components: Adaptive Labeling and Contextual Expander modules. The Adaptive Labeling module is a tracking-based refinement technique that enhances classification consistency by leveraging a tracking algorithm to assign persistent labels across frames and correct misclassifications. The Contextual Expander module improves recall for underrepresented classes by generating virtual bounding boxes with appropriate confidence scores, effectively addressing the impact of data imbalance. Experimental results show that VisionGuard improves overall mAP by 3.1% compared to baseline detectors, demonstrating its effectiveness and potential for real-world deployment in traffic surveillance systems, ultimately promoting safety and regulatory compliance.