Smart Quote Comparison System for Repair and Maintenance Vehicles

자동차 수리 및 정비를 위한 스마트 견적 비교 시스템

  • Young Bok Joo (Department of Computer Science & Engineering, Korea University of Technology & Education) ;
  • Eun Bi Son (Department of Computer Science & Engineering, Korea University of Technology & Education) ;
  • Tae San Kim (Department of Computer Science & Engineering, Korea University of Technology & Education) ;
  • Soo Ah Kim (Department of Computer Science & Engineering, Korea University of Technology & Education)
  • 주영복 (한국기술교육대학교 컴퓨터공학부) ;
  • 손은비 (한국기술교육대학교 컴퓨터공학부) ;
  • 김태산 (한국기술교육대학교 컴퓨터공학부) ;
  • 김수아 (한국기술교육대학교 컴퓨터공학부)
  • Received : 2023.11.27
  • Accepted : 2023.12.19
  • Published : 2023.12.31

Abstract

In this paper, the system is proposed and implemented to share the part number, the part name, and the vehicle type through the improvement sharing bulletin board for automobile repair and maintenance. And when photos of damage parts are uploaded to the system, the system analyzes it using a deep learning model to analyze whether it is damaged and automatically classify the type of damage. By providing repair and maintenance quotes for a significant part, the system provides economically repaired by providing comparative adjustment information on repair costs to drivers who are particularly concerned about the market prices of parts and maintenance services. Through the existing bulletin board, you can exchange and share information about parts by sharing various information on repair and maintenance. This paper provides in detail the average market price per type of damage during automobile repair and maintenance, helping drivers who do not know the details of parts and maintenance services to receive reasonable quotes by providing price information.

Keywords

Acknowledgement

이 논문은 2022년도 한국기술교육대학교 교수 교육연구진흥과제 지원에 의하여 연구되었음

References

  1. Yong Hwan Lee, Hyo Chang Ahn, "Vehicle Classification and Tracking based on Deep Learning", Journal of the Semiconductor & Display Technology, Volume 22, Issue 3, Pages 161-165, 2023. 
  2. Min Cheol Jung, "Detection and Recognition of Vehicle Brake Lights using an R-Filtering", Journal of the Semiconductor & Display Technology, Volume 10, Issue 4, Pages 95-100, 2011. 
  3. Soo Jin Oh, Chun Su Park, "Vehicle License Plate Recognition System Using Image Binarization and Template Matching", Journal of the Semiconductor & Display Technology, Volume 13, Issue 2, Pages 7-12, 2014. 
  4. Yun-hui Qu, Wei Tang, Bo Feng, "Paper Defects Classification Based on VGG16 and Transfer Learning", Journal of Korea TAPPI, Volume 53, Issue 2, Pages 5-14, 2014.  https://doi.org/10.7584/JKTAPPI.2021.04.53.2.5
  5. Kuan-Jung Chung, Cheng-Han Dai, Tung-Chun Chiang, June-Jia Xie, Ming-Tzer Lin, "Application of Recurrence Plots and VGG Deep Learning Model to the Study of Condition Monitoring of Robotic Grinding", International Journal of Precision Engineering and Manufacturing, Vol. 24, Issue 9, pp. 1675-1683, 2023.  https://doi.org/10.1007/s12541-023-00893-6
  6. Batool Shazia, Bang Junho, "Classification of Short Circuit Marks in Electric Fire Case with Transfer Learning and Fine-Tuning the Convolutional Neural Network Models", Journal of Electrical Engineering & Technology, Vol. 18, Issue 6, pp. 4329-4339, 2023.  https://doi.org/10.1007/s42835-023-01490-3