Minor Damage Detection in Cars Using Transfer Learning Techniques

The car scratch detection is a pivotal aspect of automotive maintenance and safety, crucial for preserving the visual appeal and structural integrity of vehicle exteriors. This paper investigates contemporary methodologies and advancements in car scratch detection, emphasizing both conventional and computer vision-based techniques. By scrutinizing diverse approaches including image processing algorithms, machine learning models, and sensor-driven systems, this study elucidates the efficacy and constraints of existing methodologies while delineating prospective avenues for further research and development in the realm of car scratch detection. Additionally, the paper underscores the significance of real-time detection systems and the integration of advanced technologies such as artificial intelligence and deep learning to enhance the accuracy and efficiency of car scratch detection mechanisms, ultimately contributing to the longevity and aesthetic maintenance of automotive surfaces.