Solid Waste Image Classification Using Deep Convolutional Neural Network

Abstract

Separating household waste into categories such as organic and recyclable is a critical part of waste management systems to make sure that valuable materials are recycled and utilised. This is beneficial to human health and the environment because less risky treatments are used at landfill and/or incineration, ultimately leading to improved circular economy. Conventional waste separation relies heavily on manual separation of objects by humans, which is inefficient, expensive, time consuming, and prone to subjective errors caused by limited knowledge of waste classification. However, advances in artificial intelligence research has led to the adoption of machine learning algorithms to improve the accuracy of waste classification from images. In this paper, we used a waste classification dataset to evaluate the performance of a bespoke five-layer convolutional neural network when trained with two different image resolutions. The dataset is publicly available and contains 25,077 images categorised into 13,966 organic and 11,111 recyclable waste. Many researchers have used the same dataset to evaluate their proposed methods with varying accuracy results. However, these results are not directly comparable to our approach due to fundamental issues observed in their method and validation approach, including the lack of transparency in the experimental setup, which makes it impossible to replicate results. Another common issue associated with image classification is high computational cost which often results to high development time and prediction model size. Therefore, a lightweight model with high accuracy and a high level of methodology transparency is of particular importance in this domain. To investigate the computational cost issue, we used two image resolution sizes (i.e., 225×264 and 80×45) to explore the performance of our bespoke five-layer convolutional neural network in terms of development time, model size, predictive accuracy, and cross-entropy loss. Our intuition is that smaller image resolution will lead to a lightweight model with relatively high and/or comparable accuracy than the model trained with higher image resolution. In the absence of reliable baseline studies to compare our bespoke convolutional network in terms of accuracy and loss, we trained a random guess classifier to compare our results. The results show that small image resolution leads to a lighter model with less training time and the accuracy produced (80.88%) is better than the 76.19% yielded by the larger model. Both the small and large models performed better than the baseline which produced 50.05% accuracy. To encourage reproducibility of our results, all the experimental artifacts including preprocessed dataset and source code used in our experiments are made available in a public repository.

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

“…1) Measurement Principle: The waste category classification approaches are categorized into contact-based, i.e., those having active contact with target objects [102], [108], and nocontact-based approaches [116], [117], [118], [119], [120], [121]. Furthermore, deep learning (DL)-based algorithms employing RGB and RGB-depth (RGBD) sensors have been used to detect and segment individual waste items from a densely cluttered pile [4], [78], [109], [122], [123], [124], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134], [135], [136].…”

Garbage Content Estimation Using Internet of Things and Machine Learning

“…Various standard CNN architectures have been recently proposed to perform image classification tasks with high accuracies, such as VG-GNet [17], AlexNet [18], ResNet [19] and DenseNet [20]. Nnamoko et al [5] investigated the problem of manual household garbage separation into two categories, namely, organic and recyclable. Experiments presented in this paper were conducted with Sekar's waste classification image dataset available in the Kaggle library [21].…”

“…Our study has considered the identification of daily disposed of garbage content and provided a satisfactory garbage category suitable for burnable garbage separation practice for most families in Japan. However, Nnamoko et al [5] and Mookkaiah et al [22] investigated only two kinds of garbage, i.e., Organic and recyclable, which is not enough for rational garbage separation in houses. Likewise, apart from increasing the number of classes as demonstrated by Ziouzios et al [6] and Sami et al [24], to find respective garbage categories such as (kitchen waste, other waste, hazardous waste, plastic, glass, paper or cardboard, metal, fabric, and other recyclable waste).…”

“…On the contrary, most of the previous works applied the existing standard models for the classification tasks, such as VGGNet [17], AlexNet [18], ResNet [19], and DenseNet [20]. A common issue associated with image classification using the existing standard model is high computational cost which often results in high development time and prediction model size because they are often pre-trained for more than one purpose [5]. In addition, CNN-based models are difficult to run on embedded systems suitable for garbage bins, and their architecture requires large amounts of data for training which is yet to be available.…”

“…But, again, garbage separation by the person who disposes of garbage has been widely accepted as ethical behaviour and best practice for reducing, reusing, and recycling [4]. Several existing IoT-based smart garbage systems and the classification methods using computer vision and artificial intelligence have been developed to improve household garbage management [5]- [7] and [8]. However, the existing systems have the following problems: first, they can not learn the amount of garbage disposed of each time; second, they provide a small number of garbage categories, not enough for reasonable practices of household garbage separation; and third, they can not understand the routine behaviour of garbage disposal by households.…”