Recognizing the Damaged Surface Parts of Cars in the Real Scene Using a Deep Learning Framework

Abstract

Automatically recognizing the damaged surface parts of cars can noticeably diminish the cost of processing premium assertion that leads to providing contentment for vehicle users. This recognition task can be conducted using some machine learning (ML) strategies. Deep learning (DL) models as subsets of ML have indicated remarkable potential in object detection and recognition tasks. In this study, an automated recognition of the damaged surface parts of cars in the real scene is suggested that is based on a two-path convolutional neural network (CNN). Our strategy utilizes a ResNet-50 at the beginning of each route to explore low-level features efficiently. Moreover, we proposed new mReLU and inception blocks in each route that are responsible for extracting high-level visual features. The experimental results proved the suggested model obtained high performance in comparison to some state-of-the-art frameworks.