Defect Detection and Price Estimation of Used Cars Using Deep Learning with Regression Models

Lakshmanan, Kishore (2022) Defect Detection and Price Estimation of Used Cars Using Deep Learning with Regression Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

Through various internet platforms, buying goods is becoming simple and faster in the digital age. Despite the advantages it offers consumers, there are a range of disadvantages as well, including the spread of fraudulent items and the fraud which is carried out by online vendors and sellers. These concerns can be addressed with the use of deep learning with regression technologies. Applying deep learning and machine learning methods, this research examined the identification and categorization of damage along with price estimation of secondhand cars. The most significant method for determining the faults is through deep learning models. In this domain of detecting damage, considerable research has been conducted. First, using deep learning techniques like CNN and MobileNet models, study will assess how well defects can be found in various vehicle images. After that, car price estimator is performed by applying machine learning techniques (Linear Regression and Decision Tree). For our research, the efficiency of the decision tree algorithm and convolutional neural network is excellent i.e. accuracy of 94.8 % and 95.5 % achieved respectively. Finally, the combined output of both outputs assists the consumer choose the appropriate car for purchasing. This research will also assist manufacturing firms automate inspection, government organizations categorize the severity of vehicle damage post accidents, and loan management and insurance claims.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Computer Vision; Deep Learning; Machine Learning; CNN; MobileNet; Linear Regression; Decision Tree Algorithm
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	22 Feb 2023 14:01
Last Modified:	02 Mar 2023 09:38
URI:	https://norma.ncirl.ie/id/eprint/6212

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