Powering AI-driven car damage identification based on VeHIDE dataset

Autor: Van-Dung Hoang, Nhan T. Huynh, Nguyen Tran, Khanh Le, Thi-Minh-Chau Le, Ali Selamat, Hien D. Nguyen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Information and Telecommunication, Pp 1-19 (2024)
Druh dokumentu: article
ISSN: 24751839
2475-1847
2475-1839
DOI: 10.1080/24751839.2024.2367387
Popis: In the realm of automobile insurance, the imperative of automating car damage evaluation has surged, offering streamlined assessment processes and heightened accuracy. Deep learning techniques have notably influenced vehicle damage assessment, reshaping insurance procedures. However, the primary challenge remains in crafting robust models for damage detection and segmentation. This study presents a novel contribution through the development of the Vehicle Damage Detection (VehiDE) dataset, specifically tailored for comprehensive car damage assessment. The dataset, encompassing 13,945 high-resolution images annotated across eight damage categories, serves as a foundational resource for advancing automated damage identification methodologies. Notably, VehiDE offers support for multiple tasks, including classification, object detection, instance segmentation, and salient object detection, thereby fostering versatile research avenues. Through extensive experimental analysis, including the evaluation of state-of-the-art methodologies on VehiDE, this study not only highlights the dataset's efficacy but also unveils new insights into the challenging nature of car damage assessment. Moreover, the study pioneers the exploration of salient object detection techniques in this domain, showcasing their potential in addressing irregular damage types. By offering VehiDE to the research community, we aim to catalyze advancements in the field of car damage assessment, paving the way for more accurate and efficient automated systems.
Databáze: Directory of Open Access Journals
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