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CPW-DICE: a novel center and pixel-based weighting for damage segmentation.
- Source :
- Connection Science; Dec2023, Vol. 35 Issue 1, p1-20, 20p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09540091
- Volume :
- 35
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Connection Science
- Publication Type :
- Academic Journal
- Accession number :
- 174546684
- Full Text :
- https://doi.org/10.1080/09540091.2023.2259115