1. Introduction
The growing issue of electronic waste (e-waste) poses a challenge in our society as old electronic items are often discarded instead of being recycled. To address this problem and promote sustainability, it is necessary for individuals and organisations to adopt the principles of circular economy [
1]. One viable solution is to desolder and reuse parts, thus reducing waste accumulation (
Figure 1).
An important part of the recycling process is the correct detection of objects. Automated vision-based techniques are commonly used to identify and locate objects in the workspace. These techniques often rely on artificial intelligence (AI) tools such as machine learning and deep learning [
2,
3,
4]. Vision sensors can be mounted overhead in the environment or attached to the tool that is used to manipulate objects [
5,
6,
7].
Depending on the location of the vision sensor, data are collected in the form of images for training a machine-learning model. The properties of the images in the dataset are a contributing factor in determining the complexity of the machine learning model. Images can be red–green–blue (RGB) colour or grayscale and have various resolutions. Low-resolution two-dimensional grayscale images can be converted into single-dimension arrays and input to shallow neural networks (SNNs) or support vector machines (SVMs) for classification [
8]. Feature extraction techniques such as the bag of visual words (BoVW) [
9], scale-invariant feature transform (SIFT) [
10], or principal component analysis (PCA) [
11] can be applied to reduce the dimensionality of the data before input into the classifier. However, traditional machine learning models such as SNNs and SVMs have limited accuracy.
Higher accuracy is typically achieved with deep learning models such as convolutional neural networks (CNNs) [
12,
13]. Commonly used pre-trained deep learning models employ RGB colour image inputs with resolutions such as 224 × 224 pixels (e.g., ResNet-50, MobileNet-v2, VGG-16), 227 × 227 pixels (e.g., SqueezeNet, AlexNet), or 299 × 299 pixels (e.g., Inception, Xception) [
14]. However, deep learning processes often require significant computational resources to perform tasks. These computational resources consume energy from the grid. Consequently, the ever-increasing use of AI in automated systems can place high demands on energy resources. Recently, the areas of green AI [
15,
16,
17] and energy consumption in deep learning models [
18,
19] have been discussed. The consensus is that researchers and practitioners need to be accountable for the carbon emissions of AI models they develop. Important considerations such as precision/energy trade-off, algorithm design, and network architecture are reviewed in [
15]. According to [
16], algorithmic efficiency can mitigate the upcoming energy crisis. Targeted refinement of AI algorithms can produce meaningful reductions. The focus is on energy consumption during inference (post-training execution of AI models) in [
18,
19]. While object detection and image classification have moderate consumption, an increase in their future applications will increase energy uptake. Therefore, it is important to balance the complexity of deep learning models against the potential performance improvements.
One of the common applications of computer vision is inspecting the quality and integrity of printed circuit boards (PCBs) [
10,
11,
20]. Deep neural network-based image classification techniques have been employed to detect Integrated Circuit (IC) components and verify proper positioning on the completed PCB product in [
20]. An upper limit accuracy of 92.31% was achieved with a Siamese network based on the VGG-16 deep learning model. Deep learning has also been used to recognise tiny surface-mount electronic components on PCBs in [
11]. The SqueezeNet model was selected due to its reduced number of parameters while preserving accuracy when compared with other models such as AlexNet and VGG-16. Furthermore, an optimised faster SqueezeNet architecture was designed to produce a true positive rate (TPR) of 99.99%.
Deep learning techniques have recently been utilised to detect and classify loose electrical and electronic components [
8,
13,
21,
22,
23,
24]. A customised CNN architecture has been developed to classify three categories of parts: resistors, diodes, and capacitors [
13]. The model uses six convolution layers and four pooling layers. Performance was benchmarked against four common pre-trained deep learning architectures (AlexNet, GoogleNet, ShuffleNet, and SqueezeNet). An accuracy of 98.99% was achieved using the custom CNN model. This was superior to the accuracy of the pre-trained models (92.95% to 96.67%).
Variations of the ‘you only look once’ (YOLO [
25]) deep learning architecture have also been used to classify electronic parts [
21,
22]. Fast and accurate real-time object detection is one of the major advantages of YOLO. Bounding boxes and class probabilities are directly predicted from full images in one evaluation. The YOLO-v3 and MobileNet architectures have been combined to classify four electronic components (three types of capacitors and an inductor) [
22]. The basic network architecture is based on YOLO-v3, where the Darknet53 backbone is replaced with the MobileNet architecture. Experiments yielded a mean average precision (mAP) of 0.9521 with a dataset containing 1000 images.
Furthermore, the YOLO-v4 tiny architecture has also been used for real-time electronic component detection [
21]. On its own, YOLO-v4 was able to classify twenty types of electronic components with an accuracy of 93.74%. In conjunction with a multiscale attention module (MAM) for filtering out redundant and erroneous features, the YOLO-v4 tiny network accuracy improved to 98.6%. A large dataset comprising 12,000 608 × 608 pixels RGB images was used for the classification task.
Faster R-CNN is also a potential deep learning model for detecting and classifying electronic parts [
26]. In R-CNN, a region proposal network shares full-image convolutional features with a detection network. The fully convolutional network simultaneously predicts object bounds and objectness scores at each position. However, it has been shown that Faster R-CNN performs poorly in classifying small, relatively featureless objects [
20]. Electronic components such as integrated circuits (ICs) achieved a precision and recall close to zero.
Many electronic component classifiers depend on large datasets for training models. This can be a tedious process. Hence, an approach based on a Siamese network utilising a reduced-size dataset has recently been developed [
23]. An improved VGG-16 model was proposed as the feature extraction part of the Siamese network for small sample conditions. A novel correlation loss function improved the model’s generalisation performance. A nearest neighbour algorithm was used to complete the classification work. The dataset comprised 17 different types of electronic components, with 182 images for each type. A 94% accuracy with 15 training samples was achieved in experimental results.
The application of CNNs for classifying electronic components in industrial settings has been discussed in [
24]. Electronic components were classified on an assembly line using a fixed-mounted camera. An image dataset comprising 3994 images of 11 classes was created. This translates to approximately 363 images for each class. Both custom and pre-trained networks were developed and tested. The custom baseline model used 152 × 202 pixels RGB image inputs with two convolutional layers and two dense fully connected (FC) layers. Various settings were applied to the convolutional layer filters and FC neurons. The custom network achieved up to 96.59% accuracy. The best pre-trained network accuracies varied between 97.81% and 99.03%.
Previously, a robotic system has been developed to sort three types of electronic components (capacitors, potentiometers, and IC voltage regulators) [
5,
27]. A lightweight custom CNN model was used to classify components. Low-resolution 30 × 30 pixel grayscale images were input to the model. The model had three convolutional layers and one FC layer. An accuracy of 98.1% was achieved by the custom model.
This paper extends the work presented in [
5] by investigating more lightweight models utilising various image resolutions and comparing performance with pre-trained RGB image classifier models. The purpose of the study is to empirically develop a lightweight CNN classifier that can achieve an accuracy close to pre-trained RGB classifiers for sustainable AI.
Many of the existing methods and systems for sorting electronic components do not consider factors to mitigate the complexity of their AI algorithms. There is an implicit assumption that processor power can always be increased to account for increased complexity. However, the proliferation of AI in automated systems can create an AI energy crisis [
28].
5. Conclusions
This paper presents an empirical study on classifying used electronic parts with lightweight custom CNN deep learning models for efficient classification. The classification of used parts forms part of a recycling process in a circular economy for environmental sustainability. Moreover, the high uptake of AI automated systems is placing increasing demands on energy resources. Therefore, the development of deep learning systems should balance complexity against potential performance improvements. In this respect, the lightweight custom CNN model employs grayscale images to reduce model complexity. Various resolutions of grayscale images have been tested. Lightweight models utilising 100 × 100 pixels and 224 × 224 pixels grayscale images can achieve the same accuracy as pre-trained CNN models such as ResNet-50, MobileNet-v2, VGG-16, and EfficientNet-b0. When scoring the tested deep learning models with 1% weight for execution time and 99% weight for accuracy, two of the lightweight custom CNN models ranked highest. Similar metrics for model complexity, such as floating point operations (FLOPs), could be used to score and rank deep learning models for balancing complexity and performance (accuracy).
The broader implications of this study highlight the increasing energy demands associated with the advancement of AI automated systems. As global energy consumption inevitably continues to rise, it is imperative to consider the impact of deploying advanced automation technologies. The development and deployment of deep learning models must prioritize energy efficiency for sustainable energy utilisation.
For practitioners, it is recommended to adopt lightweight models that maintain high accuracy while reducing computational complexity and energy consumption. This approach not only supports environmental sustainability but can also enhance the scalability and accessibility of AI solutions. Policymakers should incentivise research and development in energy-efficient AI technologies and establish regulations that promote the use of sustainable practices in the tech industry.
Future work will involve extending the number of part classes that the lightweight custom CNN models can detect. Further investigation into the use of various resolution grayscale images and their performance evaluation will be conducted. Balancing the performance and complexity of deep learning models is crucial for sustainable AI development. By adopting energy-efficient practices and supporting policies, sustainable AI can be realised.