CPW-DICE: a novel center and pixel-based weighting for damage segmentation

Abstract

Reliable evaluation of damage in vehicles is a primary concern in the insurance industry. Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm. During the assessment, precise damage segmentation plays a crucial role. Dent is a type of damage that can commonly occur in vehicles. It is difficult to pinpoint and tends to blend in with the background. This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims. Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting. The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations. CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks. Simultaneously, the weight mask is incorporated into DICE loss. Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss. Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation.

KEYWORDS:

• Deep learning
• image segmentation
• loss function
• car damage segmentation
• centre-based weight
• pixel-wise weight

Acknowledgements

Authors would like to thank Anadolu Insurance Company of Turkey for providing the data and equipment for this research.

Contributorship

Yunus Abdi: Co-conceptualization of the idea, Co-development of the idea, Data collection, Coding, Experiments, Results analysis, Initial draft version of the manuscript. Omer Küllü: Co conceptualization of the idea, Co-development of the idea, Supervision of the project, Finalizing the manuscript. Mehmet Kıvılcım Keleş: Supervision of the project. Finalizing the manuscript. Berk Gökberk: Assistance in the revision process both conceptually and linguistically.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available. As for the FLAME and ISBDA datasets, the datasets are available in Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle and MSNET at FLAME and ISBDA, respectively.

Additional information

Funding

This work was supported by Anadolu Sigorta.