Deep-Learning Applications in Car Damage Detection

Tanush Sharanarthi, Shreyas Iyer, Hsueh-Yao Lu and Xiaoyue Li

Chapter 12 in Digital Strategies and Organizational Transformation, 2023, pp 223-238 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This chapter proposes a car damage detection model using Convolutional Neural Networks. The automobile industry can utilize cameras to catch the defects in vehicles and provide better-quality inspection than manual methods. AI-based machines using computer vision will help make the entire supply chain process more efficient and reliable. The proposed model uses the VGG-16 Convolutional Neural Network that is optimized to provide the best accuracy and minimal loss. The model is trained and validated with a dataset containing images of vehicles that have no defects and ones that are damaged. Results show the potential of such a model being widely used in the industry since it can accurately detect damages for a diverse set of vehicles.

Keywords: Digital Technology; Digital Strategy; Information Systems; Information Technology; Organizational Changes; Internet of Things (IoT); Cloud Computing; Institutions; Innovation; Artificial Intelligence; Big Data; Data Analytics; Deep Learning; Machine Learning; Cybersecurity; Manufacturing and Production; Enterprise Architecture (search for similar items in EconPapers)
JEL-codes: O3 O32 O33 (search for similar items in EconPapers)
Date: 2023
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