Deep transfer learning benchmark for plastic waste classification (original) (raw)
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Classification of Waste Materials using CNN Based on Transfer Learning
Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation
Waste Management is important for humans as well as nature for healthy life and a clean environment. The major step for effective waste management is the segregation of waste according to its types. The advancement of technology such as hardware and artificial intelligence is used for the segregation of waste. There are several machine learning and deep learning algorithms available for image classification. Among them, Convolutional Neural Network is the most used one. The main objective of this work is to classify images of waste materials using CNN into seven categories (cardboard, glass, metal, organic, paper, plastic, and trash). Then, cardboard, organic, and paper class images are considered biodegradable waste, and other classes are considered non-biodegradable waste. The pre-trained CNN model such as InceptionV3, InceptionRes-NetV2, Xception, VGG19, MobileNet, ResNet50 and DenseNet201 have been trained and performed fine-tuning on the waste dataset. Among these models, the VGG19 model performed with less accuracy, whereas the InceptionV3 model performed with high learning accuracy. Overall, the obtained result is promising.
Sustainability
The proper handling of waste is one of the biggest challenges of modern society. Municipal Solid Waste (MSW) requires categorization into a number of types, including bio, plastic, glass, metal, paper, etc. The most efficient techniques proposed by researchers so far include neural networks. In this paper, a detailed summarization was made of the existing deep learning techniques that have been proposed to classify waste. This paper proposes an architecture for the classification of litter into the categories specified in the benchmark approaches. The architecture used for classification was EfficientNet-B0. These are compound-scaling based models proposed by Google that are pretrained on ImageNet and have an accuracy of 74% to 84% in top-1 over ImageNet. This research proposes EfficientNet-B0 model tuning for images specific to particular demographic regions for efficient classification. This type of model tuning over transfer learning provides a customized model for classification...
A Transfer Learning Approach For Efficient Classification of Waste Materials
2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), 2023
The authors of this study have used the Waste Classification Dataset to build a highly accurate model that classified rubbish into two distinct groups in an effort to address the problem of waste classification for various classes of discarded material. VGG16, MobileNetV2, and a baseline 6 layer CNN model are used in the experiments. The VGG16 model have achieved 96.00% accuracy, while the MobileNetV2 model achieved 95.51%, and the baseline CNN model achieved 90.61% accuracy. The garbage in the input picture can be correctly classified by the neural network model. The experimental findings are compared to other studies in the same area. In addition, LIME is also implemented to make our models's prediction more explainable. This investigation's experimental applications are geared on facilitating more precise trash classification.
A Plastic Contamination Image Dataset for Deep Learning Model Development and Training
AgriEngineering
The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of i...
Reach on Waste Classification and Identification by Transfer Learning and Lightweight Neural Network
2020
Using machine learning or deep learning to solve the problem of garbage recognition and classification is an important application in computer vision, but due to the incomplete establishment of garbage datasets and the poor performance of complex network models on smart terminal devices, the existing garbage classification models The effect is not good.This paper presents a waste classification and identification method base on transfer learning and lightweight neural network. By migrating the lightweight neural network MobileNetV2 and rebuild it, The reconstructed network is used for feature extraction, and the extracted features are introduced into the SVM to realize the identification of 6 types of garbage. The model was trained and verified by using 2527 pieces of garbage labeled data in the TrashNet dataset, which ultimately resulted in a classification accuracy of 98.4% of the method, which proves that the method can effectively improve the classification accuracy and time and...
A Fusion of Three Custom-Tailored Deep Learning Architectures for Waste Classification
2022
Population growth is exploding exponentially in this current world. Due to this escalating population and urbanization, the overall amount of waste is experiencing a rapid growth worldwide. Consequently, plenty of waste contributes to climate change, affecting our ecosystems and species. Fortunately, trash management would help alleviate some of these effects since a large quantity of trash is highly biodegradable and recyclable. However, classifying waste manually based on its contents is highly expensive and time-consuming. This is why the classification of wastes based on their contents is a critical criterion for ensuring cost-effective performance throughout recycling procedures. This paper proposes a hybrid deep-learning framework to classify waste into four categories: paper, glass, plastic, and organic. To address the lack of sufficient data, we used the albumentation function to augment the data. Later, in order to remove any duplicates that might have existed in the updated dataset, we also applied an image hashing technique that tackles the problem of overfitting. After preprocessing, we integrate three different models (2 EfficientNet models with noisystudent and imagenet and a custom convolutional neural network model) and provide prediction, and a heatmap of the eXplainable Artificial Intelligence (X-AI) generated images based on the test dataset to improve the trustworthiness of the inference. In comparison to various earlier state-of-the-art studies in the area of waste management, our technique performed substantially better, scoring at around 97% accuracy.
Dual-Branch CNN for the Identification of Recyclable Materials
2021
The classification of recyclable materials, and in particular the recovery of plastic, plays an important role in the economy, but also in environmental sustainability. This study presents a novel image classification model that can be efficiently used to distinguish recyclable materials. Building on recent work in deep learning and waste classification, we introduce the so-called “Dual-branch Multi-output CNN”, a custom convolutional neural network composed of two branches aimed to i) classify recyclables and ii) distinguish the type of plastic. The proposed architecture is composed of two classifiers trained on two different datasets, so as to encode complementary attributes of the recyclable materials. In our work, the Densenet121, ResNet50 and VGG16 architectures were used on the Trashnet dataset, along with data augmentation techniques, as well as on the WaDaBa dataset with physical variation techniques. In particular, our approach makes use of the joint utilization of the data...
PLASTIC DETECTION AND CLASSIFICATION USING DEEP LEARNING NEURAL NETWORK
IRJET, 2022
Although plastic pollution is one of the most significant environmental issues today, there is still a gap in information about monitoring the local distribution of plastics, which is needed to prevent its negative effects and to plan mitigation measures. Plastic waste management is a global challenge. Manual waste disposal is a complex and expensive process, which is why scientists make and learn automated planning methods that increase the efficiency of the recycling process. Plastic waste can be automatically selected from the waste disposal belt using imaging processing techniques and artificial intelligence, especially indepth reading, to improve the recycling process. Waste disposal techniques and procedures are used in large groups of materials such as paper, plastic, metal, and glass. However, the biggest challenge is to differentiate between different types of objects in the group, for example, to filter different colors of glass or plastic. It is important because it is possible to recycle certain types of plastic (PET can be converted into polyester material). Therefore, we must look at ways to separate this waste. One of the opportunities is the use of indepth learning and convolutional neural networks. In household rubbish, the main problem is plastic parts, and the main types are polyethylene, polypropylene, and polystyrene. The main problem considered in this article is to create an automated plastic waste disposal system, which can categorize waste into four categories, PET, PP, HDPE, and LDPE, and can work in a filter plant or citizen home. We have suggested a method that can work on mobile devices to identify waste that can be useful in solving urban waste problems.
Classification of recyclable waste using deep learning architectures
FIRAT UNIVERSITY JOURNAL OF EXPERIMENTAL AND COMPUTATIONAL ENGINEERING, 2022
Managing waste in big cities is a big problem. Wastes are dangerous in terms of causing environmental pollution and affecting human health. In particular, solid wastes such as glass and plastic do not dissolve in the soil for a long time and pollute the environment. By recycling such solid wastes, the surrounding waste can be reduced. Therefore, it is important to classify waste and to recycle the separated waste. In this study, a data set consisting of 22500 waste images was used. The data set contains color image data with a size of 227 x 227 pixels. The data used in the study are divided into two as organic and recyclable waste. This study proposes a deep learning-based system for classifying waste. With such a system, wastes can be classified and recycled. The data was trained with the ResNet 50 architecture and the CNN architecture created to classify waste, and accuracy rates were compared. The CNN architecture created to classify waste is more successful for this data set with an accuracy rate of 91.84%.
A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification
IEEE Access, 2023
In response to the growing waste problem caused by industrialization and modernization, the need for an automated waste sorting and recycling system for sustainable waste management has become ever more pressing. Deep learning has made significant advancements in image classification, making it ideally suited for waste sorting applications. This application depends on the development of a suitable deep learning model capable of accurately categorizing various categories of waste. In this study, we present RWC-Net (recyclable waste classification network), a novel deep learning model designed for the classification of six distinct waste categories using the TrashNet dataset of 2,527 images of waste. The performance of our model is subjected to intensive quantitative and qualitative evaluations and is compared to various state-of-art waste classification techniques. The proposed model outperformed several state-ofthe-art models by obtaining a remarkable overall accuracy rate of 95.01 percent. In addition, it receives high F1-scores for each of the six waste categories: 97.24% for cardboard, 96.18% for glass, 94% for metal, 95.73% for paper, 93.67% for plastic, and 88.55% for litter. The reliability of the model is demonstrated qualitatively through the saliency maps generated by Score-CAM (class activation mapping) model, which provide visual insights into its performance across various waste categories. These results highlight the model's accuracy and demonstrate its potential as an effective automated waste classification and management solution.