Multimodal Deep Learning Model for the Automated Evaluation of Repossessed Motorcycles

The application of artificial intelligence (AI) in vehicle maintenance and damage assessment has received growing attention, particularly for minimizing insurance claim leakage, expediting accident compensation, and improving the evaluation of second-hand vehicles. However, most AI-based systems focus on four-wheeled vehicles and rely on single-modal input, limiting their applicability to other types of vehicles. To address this gap, we present the Automated Repossessed Motorcycle Assessment System (ARMAS), which employs a multi-modal deep learning framework that integrates image, audio, and tabular data to classify motorcycle conditions on a six-point scale (A–F), corresponding to resale value brackets. The system combines convolutional neural networks (CNNs) for image processing, gradient boosting trees for structured data, and neural audio feature extraction, all integrated through ensemble learning to enhance robustness and predictive accuracy. Trained on a real-world dataset from a motorcycle finance company, ARMAS achieved a mean absolute error (MAE) of 0.612, significantly outperforming manual inspections. It also reduces assessment time from 30–90 minutes to under one minute, representing a substantial gain in efficiency by industry standards. These results underscore the practical value of AI in real-time, scalable motorcycle assessment and highlight the broader potential of deep learning in asset valuation and multimodal systems.