On the Application of DiffusionDet to Automatic Car Damage Detection and Classification via High-Performance Computing

Claim management is a critical process for insurance companies, requiring fairness, transparency, and efficiency to maintain policyholder trust and minimize financial impact. In our previous work, we introduced Insoore AI, an insurtech solution leveraging deep learning-based computer vision to automate car damage recognition and localization from user-provided pictures. While this approach demonstrated the potential of AI in claims management, it faced limitations in terms of performance and computational efficiency due to resource constraints. In this study, we present an improved version of Insoore AI, enabled by the High-Performance Computing (HPC) resources offered by the Booster module of LEONARDO HPC system located at the CINECA datacenter in Bologna, Italy. By leveraging the advanced computational capabilities of the above-mentioned HPC infrastructure, we trained larger and more complex deep learning models, processed higher-resolution images, and significantly reduced training and inference times. Our results show marked performance improvements in terms of damage detection, paving the way for more efficient, more effective and scalable claims management solutions. This work underscores the transformative potential of HPC resources in advancing AI-driven innovations in the insurance sector and is to be regarded as an improvement on the contribution of our previous work, enabled by relying on the DiffusionDet architecture and on a Swin Transformer backbone to solve the problem of automatic car damage detection and classification.