Work

Evaluation of Contrastive Learning for Electronic Component Detection

Year: 2023

Type: conference-paper

Abstract: The rapid growth of electronic waste (e-waste) has led to an urgent need for efficient recycling processes to recover valuable materials and reduce environmental impact.Waste Printed Circuit Boards (WPCBs) constitute significant e-waste and contain valuable components and precious metals.Computer vision systems can automate the classification, disassembly, and recycling of WPCBs.However, obtaining large annotated datasets for machine learning in this domain is costly and often unavailable.This paper investigates using few-shot and supervised contrastive learning in electronic component detection.We propose a model incorporating contrastive learning components for detecting electronic components in scenarios with limited training data or annotated labels.Our experimental results show that, in limited-data scenarios, contrastive learning outperforms the original versions of Faster R-CNN object detector.This study contributes to developing efficient recycling solutions for e-waste management and resource recovery.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2023.es2023-167

Authors: Leandro Silva, Agostinho Junior, Bruno Fernandes, George Azevedo, Sérgio Oliveira

Institutions: Instituto Federal de Educação Ciência e Tecnologia da Paraíba, Universidade de Pernambuco

Language: English

Topic: Recycling and Waste Management Techniques

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: bronze

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco