Robust anomaly detection through multi-modal autoencoder fusion for small vehicle damage detection

Wear and tear detection in fleet and shared vehicle systems is a critical challenge, particularly in rental and car-sharing services, where minor damage, such as dents, scratches, and underbody impacts, often goes unnoticed or is detected too late. Currently, manual inspection methods are the defaul...

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Veröffentlicht in:Machine learning with applications Jg. 22; S. 100794
Hauptverfasser: Khan, Sara, Yüksel, Mehmed, Kirchner, Frank
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2025
Elsevier
Schlagworte:
Autoencoders
Automotive
Deep learning
Multi-modal
Sensors
Signal processing
Deep learning
Signal processing
Sensors
Multi-modal
Autoencoders
Automotive
ISSN:2666-8270, 2666-8270
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Zusammenfassung:Wear and tear detection in fleet and shared vehicle systems is a critical challenge, particularly in rental and car-sharing services, where minor damage, such as dents, scratches, and underbody impacts, often goes unnoticed or is detected too late. Currently, manual inspection methods are the default approach, but are labour-intensive and prone to human error. In contrast, state-of-the-art image-based methods are less reliable when the vehicle is moving, and they cannot effectively capture underbody damage due to limited visual access and spatial coverage. This work introduces a novel multi-modal architecture based on anomaly detection to address these issues. Sensors such as Inertial Measurement Units (IMUs) and microphones are integrated into a compact device mounted on the vehicle’s windshield. This approach supports real-time damage detection while avoiding the need for highly resource-intensive sensors. We developed multiple variants of multi-modal autoencoder-based architectures and evaluated them against unimodal and state-of-the-art methods. Our multi-modal ensemble model with pooling achieved the highest performance, with a Receiver Operating Characteristic-Area Under Curve (ROC-AUC) of 92%, demonstrating its effectiveness in real-world applications. This approach can also be extended to other applications, such as improving automotive safety. It can integrate with airbag systems for efficient deployment and help autonomous vehicles by complementing other sensors in collision detection.
ISSN:2666-8270
2666-8270
DOI:10.1016/j.mlwa.2025.100794