Authors : Sai Madhu; Bharathi Maddikatla; Ranjitha Padakanti; Vineel Sai Kumar Rampally; Shirish Kumar Gonala

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/2rhn8efj

Scribd : https://tinyurl.com/297yn5b8

DOI : https://doi.org/10.38124/ijisrt/25apr1759

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Abstract : This project presents an automated framework for vehicle damage evaluation employing deep learning methodologies, designed to optimize assessment procedures within automotive service environments. By implementing the YOLOv9 computational vision architecture, the system enables rapid identification of vehicular damage components through advanced pattern recognition, reducing reliance on labor-intensive manual inspections. The model underwent training on an extensive curated dataset comprising 8,450 annotated images capturing diverse damage morphologies across multiple vehicle perspectives, including frontal collisions, lateral impacts, and rear-end accidents. The framework integrates physics-informed augmentation strategies to enhance environmental adaptability, particularly addressing challenges posed by variable lighting conditions and reflective surfaces. A modular processing pipeline facilitates scalable deployment through quantization techniques optimized for edge computing devices, demonstrating practical applicability in service center operations. The system incorporates a web-based interface enabling real-time damage visualization and automated report generation, significantly streamlining technician workflows. Experimental results indicate substantial improvements in inspection efficiency, with the YOLOv9 architecture achieving 87% mean average precision ([email protected]) while maintaining computational efficiency. Quantized model variants exhibited a 68% reduction in memory footprint with minimal accuracy degradation. Field validations conducted across multiple service centers confirmed the system's operational effectiveness, highlighting strong correlations between model complexity, training duration, and real-time detection capabilities. This research establishes foundational insights for future advancements in 3D damage reconstruction and adaptive learning systems within automotive diagnostics.

Keywords : Computational Damage Assessment, YOLOv9 Architecture, Automotive Computer Vision, Edge AI Optimization, Service Process Automation, Neural Network Quantization.

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