An Empirical Study on Lightweight CNN Models for Efficient Classification of Used Electronic Parts

Praneel Chand () and Mansour Assaf
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Praneel Chand: Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
Mansour Assaf: School of Information Technology, Engineering, Mathematics and Physics, The University of the South Pacific, Suva 1168, Fiji

Sustainability, 2024, vol. 16, issue 17, 1-18

Abstract: The problem of electronic waste (e-waste) presents a significant challenge in our society as outdated electronic devices are frequently discarded rather than recycled. To tackle this issue, it is important to embrace circular economy principles. One effective approach is to desolder and reuse electronic components, thereby reducing waste buildup. Automated vision-based techniques, often utilizing deep learning models, are commonly employed to identify and locate objects in sorting applications. Artificial intelligence (AI) and deep learning processes often require significant computational resources to perform automated tasks. These computational resources consume energy from the grid. Consequently, a rise in the use of AI can lead to higher demand for energy resources. This research empirically develops a lightweight convolutional neural network (CNN) model by exploring models utilising various grayscale image resolutions and comparing their performance with pre-trained RGB image classifier models. The study evaluates the lightweight CNN classifier’s ability to achieve an accuracy comparable to pre-trained red–green–blue (RGB) image classifiers. Experiments demonstrate that lightweight CNN models using 100 × 100 pixels and 224 × 224 pixels grayscale images can achieve accuracies on par with more complex pre-trained RGB classifiers. This permits the use of reduced computational resources for environmental sustainability.

Keywords: convolutional neural network (CNN); deep learning; used electronic components; computer vision; environmental sustainability; green AI; computational efficiency (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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