Feature-Based Transfer Learning for High-Value Component Recovery in Electric Vehicles: An InceptionV3 Model Evaluation

Yang Luo, Yifeng Xu, Fan Zhang, Ying Tuan Lo, Geng Lyu, Andrew Tan, Anwar P.P.Abdul Majeed*, Yuyi Zhu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

The worldwide transition to electric vehicles (EVs) has resulted in a substantial rise in the quantity of end-of-life (EoL) EVs that need effective recycling methods. This study examined a method of transfer learning that uses features to classify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different electronic control units (ECUs). The photos were processed using pre-trained InceptionV3 convolutional neural network (CNN) models to identify distinctive features. The performance of four classifiers, namely the Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), and Naive Bayes (NB), was tested using the collected features. The dataset was partitioned into training, validation, and test sets using a 70:15:15 stratified split to guarantee an equitable distribution of all classes. The InceptionV3-SVM pipeline achieved the highest performance, with training, validation, and test accuracies of 100%, 97%, and 97%, respectively. Other classifiers also demonstrated strong performance, with validation and test accuracies exceeding 94%. The high accuracy and generalization capabilities of the InceptionV3-SVM pipeline indicate its potential for practical deployment in sustainable manufacturing processes. This study provides a foundation for further research in the automated sorting and recovery of high-value electronic components from EVs, potentially extending to a broader range of electronic components and applications. The findings highlight the effectiveness of transfer learning techniques in enhancing the efficiency and accuracy of recycling operations in the automotive industry.

Original languageEnglish
Title of host publicationSelected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
EditorsWei Chen, Andrew Huey Ping Tan, Yang Luo, Long Huang, Yuyi Zhu, Anwar PP Abdul Majeed, Fan Zhang, Yuyao Yan, Chenguang Liu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages368-375
Number of pages8
ISBN (Print)9789819639489
DOIs
Publication statusPublished - 2025
Event2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Suzhou, China
Duration: 22 Aug 202423 Aug 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1316 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Country/TerritoryChina
CitySuzhou
Period22/08/2423/08/24

Keywords

  • Deep Learning
  • Electric Vehicles (EVs)
  • End-of-Life (EoL)
  • Feature-Based Transfer Learning
  • Machine Learning
  • Sustainable Manufacturing

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