Vehicle Damage Detection Using Artificial Intelligence: A Systematic Literature Review

Abstract

Automating vehicle damage detection is essential for automotive industry applications like insurance claims, online sales, and repair cost estimates, addressing the labor-intensive, time-consuming, and error-prone nature of current manual inspections. This systematic literature review explores the use of artificial intelligence (AI), particularly deep learning-based algorithms, to improve the accuracy and efficiency of damage detection under dynamic and challenging conditions specific to the requirements of our industry partners. The review is structured around five key research questions and includes extensive empirical evaluations to identify gaps and challenges in existing methods. Findings reveal significant potential for AI to automate and enhance the damage detection process but also highlight areas requiring further research and development. The review discusses these gaps in detail, providing a comprehensive foundation for future work in this field. Furthermore, the review findings are intended to guide both our research and the broader research community in advancing the practical application of AI for vehicle damage assessment. The insights gained from this review are crucial for developing robust AI solutions that can operate effectively in real-world scenarios, ultimately improving operational efficiency and customer experience in the automotive industry.