Vehicle damage validation using symmetry-based deep learning

Abstract

A computer-implemented method and system for validating vehicle damage detection utilizes symmetry-based analysis of opposing vehicle sides. The method comprises receiving images from opposing sides of a vehicle, detecting a damage region in one image, and computing deep learning feature vectors for the damage region and a corresponding region in the opposite image. These feature vectors represent learned visual patterns that discriminate between damage and normal vehicle features. A similarity measure is computed between the feature vectors, and the detected damage is validated based on this measure. The system includes sensors for image capture, processors, and memory storing instructions to execute the method. This approach leverages vehicle symmetry to reduce false positives, compensate for environmental variations, and improve damage detection accuracy. The method can adapt to asymmetric vehicle positioning and varying environmental conditions, providing robust performance in real-world scenarios.