Automated Damage Inspection in Vehicle Headlights Using U-Net and Resnet50 - Morais, Kevila Cezario de; Komati, Karin Satie; Gazolli, Kelly Assis de Souza (Anais do Workshop de Visão Computacional (WVC); 2023: Anais do XVIII Workshop de Visão ComputacionalDO - 10.5753/wvc.2023.27526, 2023)
The image analysis of vehicle damage is a procedure performed by insurance companies to determine whether the policy covers the service or not. In the case of damages in the headlights, the company receives a picture of the vehicle and a specialist analyzes the damage. This article proposes a system for the detection and classification of vehicle headlight images to automate the inspection. The method is based first on the U-Net structure for detecting the headlight in the image and then on the Resnet50 structure for classifying the damage. The U-Net database is made up of 2,000 vehicle images and 2,000 masks with headlight detection. Resnet50’s database is made up of 2,000 images divided into 4 classes: broken, blurred, infiltrated, or undamaged. The results obtained in the test had an IOU of 70 percent in the detection and an accuracy of 76 percent in the classification.