Evaluation of Vehicle Fitness Using Convolutional Neural Networks

This paper presents a vehicle fitness test using a Convolutional Neural Network (CNN). All commercial vehicles must periodically endure fitness tests in accordance with the Motor Vehicle Act, based on the age of the vehicle. Bring the cars to fitness facilities for inspection and certification. Additionally, there are only a few centres in each district, so there are frequently lengthy lines and some hassles with the process. In order to reduce human effort and increase productivity, a creative answer is needed. To make this procedure easier for everyone, an efficient solution is needed. We needed to create an online system because everything in this day and age is online, making it easier to manage this process and requiring less human effort. This device allows us to compare to the current system and gives vehicle owners the best means to finish the vehicle fitness test. The online system, where the entire fitness testing will be automated, is implemented because the current system takes more time to complete a piece of work. The system would respond in accordance with the criteria if the damage was discovered, which was our main objective. He was free to go through more scrutiny at the inspection centre if no damage was discovered. As a result, we have created a system that perfectly fits the criteria for properly conducting vehicle fitness testing. the procedures required for an automated inspection of vehicle damage.

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Publication Details

Published
2024-10-19
DOI
https://doi.org/10.1109/cvmi61877.2024.10782560
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.27
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Evaluation of Vehicle Fitness Using Convolutional Neural Networks

Prathamesh Dalvi, Nidesh Alimkar, Dhiraj Foujdar, Dhaval Deshkar et al.
1 citations
Traffic Prediction and Management Techniques
0.27
article

Evaluation of Vehicle Fitness Using Convolutional Neural Networks

Prathamesh Dalvi, Nidesh Alimkar, Dhiraj Foujdar, Dhaval Deshkar, Suraj Raskar, Rabel Guharoy
article en
1 citations

Abstract

This paper presents a vehicle fitness test using a Convolutional Neural Network (CNN). All commercial vehicles must periodically endure fitness tests in accordance with the Motor Vehicle Act, based on the age of the vehicle. Bring the cars to fitness facilities for inspection and certification. Additionally, there are only a few centres in each district, so there are frequently lengthy lines and some hassles with the process. In order to reduce human effort and increase productivity, a creative answer is needed. To make this procedure easier for everyone, an efficient solution is needed. We needed to create an online system because everything in this day and age is online, making it easier to manage this process and requiring less human effort. This device allows us to compare to the current system and gives vehicle owners the best means to finish the vehicle fitness test. The online system, where the entire fitness testing will be automated, is implemented because the current system takes more time to complete a piece of work. The system would respond in accordance with the criteria if the damage was discovered, which was our main objective. He was free to go through more scrutiny at the inspection centre if no damage was discovered. As a result, we have created a system that perfectly fits the criteria for properly conducting vehicle fitness testing. the procedures required for an automated inspection of vehicle damage.

Rashtriya Raksha University (IN), Bharati Vidyapeeth (Deemed to be University) (IN)
Openalex Percentile: Top 41%
Traffic Prediction and Management Techniques
0.27
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Evaluation of Vehicle Fitness Using Convolutional Neural Networks — Prathamesh Dalvi, Nidesh Alimkar, et al. · (2024) | TGRS Research Map | TGRS