Computer Vision based Automated Spare Part Finder App

1. INTERNATIONAL CONFERENCE ON
APPLIED ARTIFICIAL INTELLIGENCE AND
COMPUTING
(ICAAIC 2022)
COMPUTER VISION BASED
AUTOMATED SPARE PART FINDER APP
CHINIAH AATISH, GEERISHRAJ JUGROOP, PARISH SEEGOLUM,
BAICHOO BIBI HUMAÏRA
UNIVERSITY OF MAURITIUS

2. CONTENTS
• Introduction
• Aims and Objectives
• The System
• Tools used
• Results
• Conclusion

3. INTRODUCTION
• Identifying/finding spare parts for cars can be quite time consuming
as it requires technical knowledge about mechanics.
• The main issues that car owners have are: little information about the
name of spare part, make and model of car and where to obtain the
parts from and also no online comparator available for spare parts as
there are for plane tickets or car insurance.
• Given the recent uptake of websites for business, car owners would
need to browse through different shops and go through the
complicated search to find the desired products.
• Our solution, a mobile app, provides identification of a spare part
information through image processing using the camera of
smartphone and a deep learning model. Once the metadata of the
spare part required is obtained, web mining technique is used to
obtain a list of websites from where the spare part can be purchased.

4. AIMS AND OBJECTIVES
• To implement a mobile application that would enable the
identification of a spare part using its image which will be used to
generate a list of suppliers.
• To conduct a detailed analysis of similar existing object detection
system.
• To build a dataset of spare parts.
• To train a neural network model to detect and classify images of
spare parts.
• To create a mobile app and deploy model.
• To create a web scrapper to display a list of website from where
spare part can be purchased.

5. THE SYSTEM

6. TOOLS USED
• Flutter SDK 2.2.3
• Labelimg
• Roboflow
• TensorFlow
• Android Studio
• TFLite flutter plugin

7. RESULTS
Scraper Results
View After Image
Selection

8. TRAINING RESULTS

9. ACCURACY OF TEST RESULTS

10. INFERENCE RESULTS

11. INFERENCE RESULTS

12. INFERENCE RESULTS

13. PERFORMANCE EVALUATION
Before running App While using App

14. CONCLUSION
•In this work, we demonstrate that how several
technologies could be brought together to
solve real-life issues. Using Computer Vision,
our implementation is capable of identifying
spare parts using a custom built dataset. A list
of websites from where to buy the specific
spare part is then presented to the user as
links.

15. Thank you for your kind
attention!