Convolutional Neural Networks for vehicle damage detection

Abstract

Vehicle damages are increasingly becoming a liability for shared mobility services. The large number of handovers between drivers demands for an accurate and fast inspection system, which locates small damages and classifies these into the correct damage category. To address this, a damage detection model is developed to locate vehicle damages and classify these into twelve categories. Multiple deep learning algorithms are used, and the effect of different transfer learning and training strategies is evaluated, to optimize the detection performance. The final model, trained on more than 10,000 damage images, is able to accurately detect small damages under various conditions such as water and dirt. A performance evaluation with domain experts shows, that the model achieves comparable performance. In addition, the model is evaluated in a specially designed light street, indicating that strong reflections complicate the detection performance.