A Hybrid Soft Computing Framework for Robust Classification of Heavy Transport Vehicles in Visual Traffic Surveillance

The efficient classification of transport vehicles is critical to the optimization of modern transportation systems, yet significant challenges persist, particularly in distinguishing Heavy Transport Vehicles (HTVs) from Light Transport Vehicles (LTVs).These challenges arise due to considerable variations in vehicle size, shape, orientation, and external factors such as camera perspective, lighting conditions, and occlusions.In this study, a novel classification framework is proposed, integrating geometric feature extraction with a soft computing approach based on fuzzy logic.Key geometric attributes, including bounding box length, width, area, and aspect ratio, are extracted through image processing techniques.Initial classification is performed via threshold-based rules to eliminate non-HTV instances using predefined feature thresholds.To address uncertainties inherent in real-world surveillance conditions, fuzzy logic inference is subsequently applied, enabling flexible and robust decision-making in the presence of imprecise or noisy data.This hybrid methodology, combining deterministic thresholding and soft computing principles, enhances classification reliability and adaptability under diverse environmental and operational conditions.Extensive real-world experiments have been conducted to validate the proposed framework, demonstrating superior performance in terms of accuracy, robustness, and computational efficiency when compared with conventional classification methods.The results underscore the potential of the framework for deployment in intelligent traffic monitoring systems where precise vehicle categorization is essential for traffic management, infrastructure planning, and safety enforcement.