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

Description

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 meth-ods. This study examined a method of transfer learning that uses features to clas-sify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different elec-tronic control units (ECUs). The photos were processed using pre-trained Incep-tionV3 convolutional neural network (CNN) models to identify distinctive fea-tures. 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 gener-alization 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 effective-ness of transfer learning techniques in enhancing the efficiency and accuracy of recycling operations in the automotive industry.

Period	22 Aug 2024
Event title	International Conference on Intelligent Manufacturing and Robotics 2024
Event type	Conference
Location	Taicang, Suzhou, ChinaShow on map
Degree of Recognition	International