Automatic Car Service Recommendation System using Machine Learning Techniques

M. A. R. Kumar*, Mohammed Abdullah Khan**, Gundlapally Siri Reddy***, Ramavath Tarun****, Sujith Yadav*****
*-***** Sreyas Institute of Engineering and Technology, Hyderabad, Telangana, India.
Periodicity:October - December'2022
DOI : https://doi.org/10.26634/jip.9.4.19241

Abstract

The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle.

Keywords

Car Maintenance Recommendation System, Efficient Time Utilization, Damage Detection with Specification, Dirtiness Detection, Machine Learning.

How to Cite this Article?

Kumar, M. A. R., Khan, M. A., Reddy, G. S., Tarun, R., and Yadav, S. (2022). Automatic Car Service Recommendation System using Machine Learning Techniques. i-manager’s Journal on Image Processing, 9(4), 46-54. https://doi.org/10.26634/jip.9.4.19241

References

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