Effective deep learning aided vehicle classification approach using Seismic Data

Intelligent transportation systems (ITSs) significantly enhance traffic safety and management globally. A critical component of these systems is vehicle classification (VC), which supports vital applications such as congestion control, traffic monitoring, accident avoidance, etc. Traditional classification algorithms rely heavily on visual or sensor-based data (e.g., radar or image signals), often compromised by adverse weather, poor lighting, or occlusion. To address these limitations, this paper introduces a novel VC technique that leverages seismic data to detect vehicle-generated vibrations, thereby reducing susceptibility to environmental conditions and privacy concerns. We propose a self-supervised contrastive learning approach for seismic signal classification, eliminating the need for labeled data for feature extraction and representation. Our method employs specialized data augmentation techniques to create positive and negative pairs, enhancing feature representation. The encoder network extracts meaningful features from seismic signals while the projection head refines latent space representation. Training with contrastive loss ensures that positive pairs are closely aligned and negative pairs are distinctly separated in the latent space. Experimental results validate the efficacy of our approach, achieving state-of-the-art performance using seismic signal classification tasks with limited training data. Our approach achieves an impressive accuracy of 99.8%, underscoring its potential for robust and precise VC in ITSs using seismic data, particularly in data-scarce scenarios. The code is publicly available at https://github.com/MohamedHassanSaad/Vehicle-Classification.git.