CPW-DICE: a novel center and pixel-based weighting for damage segmentation
Connection Science, cilt.35, sa.1, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 35 Sayı: 1
- Basım Tarihi: 2023
- Doi Numarası: 10.1080/09540091.2023.2259115
- Dergi Adı: Connection Science
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Linguistics & Language Behavior Abstracts, Psycinfo, Directory of Open Access Journals
- Anahtar Kelimeler: car damage segmentation, centre-based weight, Deep learning, image segmentation, loss function, pixel-wise weight
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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.