Implementation Convolutional Neural Network for Visually Based Detection of Waste Types

Convolutional neural networks (CNNs) are being used in this paper to investigate waste classification. The study compared the categorization of three waste categories (plastic, paper, and metal) using a CNN and a Support Vector Machine (SVM). The results showed that while the SVM had an accuracy of 94.8%, the CNN's accuracy was lower at 83%. Another study divided waste into six categories and showed that the classification accuracy was 22% with a CNN, while the SVM had a test accuracy of 63%. The authors noted challenges in optimizing the hyperparameters, which prevented the CNN from being trained to its full potential. However, they concluded that the CNN should attain higher accuracy levels than the SVM once these challenges are overcome. The authors also used two popular CNN architectures, VGG16 and FastNet-34, in their proposed model for waste classification. The dataset used for the model was provided by Kaggle and consisted of 22564 waste photos divided into recyclable (R) and organic (O) categories. The results of the proposed model showed improved accuracy in waste classification compared to previous studies. The study highlights the potential for the use of CNNs in the field of waste management but highlights the importance of overcoming challenges in hyperparameter optimization for higher computer vision accuracy.

A deep convolutional neural network to simultaneously localize and recognize waste types in images

In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission.

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision–based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.

The rapid growth of urbanization and economic development has led to a significant increase in household waste, highlighting the necessity for developing sustainable waste management processes. Traditional waste sorting methods are based on manual processes, leading to high labor costs and low efficiency. This makes waste management systems less environmentally and economically sustainable, especially in densely populated areas. Furthermore, these methods reduce the efficiency of recycling processes and waste valuable resources by making it difficult to correctly sort waste by type. The time-consuming nature of manual methods and the risk of human error necessitate technological solutions to improve these processes. In this context, artificial intelligence-based technologies play an important role in waste management processes. Artificial intelligence reduces costs and increases efficiency by minimizing human errors through fast and accurate sorting processes. This study employed a publicly accessible dataset containing 22,564 images classified as recyclable and organic waste to train Convolutional Neural Network (CNN) models. The models’ performance was assessed using metrics such as validation accuracy and validation loss. The findings indicate that the Optimized Convolutional Neural Network (OCNN) model achieved superior generalization capacity with a validation accuracy of 90.41%, outperforming the traditional CNN model, which attained 88.26%. This study aims to increase environmental sustainability and improve economic efficiency in waste management by using innovative methods. The proposed approaches are developed to increase the efficiency of waste classification processes, thereby supporting the conservation of natural resources and promoting higher recycling rates.

Urban waste management poses significant challenges due to the growing volume and complexity of waste generated in cities. Effective classification of waste into degradable and non-degradable categories is critical for optimizing waste disposal, promoting recycling, and mitigating environmental impacts. This report presents a comprehensive exploration of an adaptive machine learning algorithm for image-based waste classification, designed to enhance municipal waste management systems. The proposed approach integrates advanced image processing techniques with machine learning models, such as Convolutional Neural Networks (CNNs) and their efficient variants, to classify waste with high accuracy. A citizen reporting interface allows users to capture and upload waste images, which are processed in real-time by a centralized system. The adaptive algorithm incorporates preprocessing steps, feature extraction, and model inference to deliver actionable insights, enabling municipal authorities to deploy appropriate waste collection mechanisms promptly. Through a detailed literature review, the report examines existing methodologies, benchmarks their performance, and identifies key challenges, including data quality, scalability, and integration with urban infrastructure. Case studies from global implementations illustrate the transformative potential of AI-driven waste management systems. Furthermore, the report outlines future directions, such as leveraging advanced deep learning architectures, real-time deployment using edge computing, and expanding classification to include hazardous and recyclable waste categories. This work contributes to the growing field of smart waste management by demonstrating how machine learning can address critical urban challenges. By fostering collaboration between technology and sustainability, the proposed framework lays the foundation for cleaner and more sustainable cities.

Control Chart Pattern Recognition (CCPR) plays a crucial role in maintaining product quality. This paper presents some recognition models that utilizes a custom convolutional network alongside pretrained VGG19, MobileNet, and LeNet networks to identify Control Chart Patterns (CCP) for autocorrelated processes. The suggested architectures autonomously extract features from input data, in contrast to conventional methods that necessitate manual feature engineering. This study addresses the challenge of training deep networks with insufficient training data by employing transfer learning to refine a VGG19, MobileNet, and LeNet model, for a new CCPR task. The comparison of the performance between pre-trained networks and the extended convolutional network as a 2D CNN, which does not utilize transfer learning, indicates that pre-trained networks attain superior recognition accuracies with a smaller training data. On the other hand, the pretrained VGG19 demonstrates superior performance when compared to 1D CNN and conventional machine learning techniques, highlighting the advantages of utilizing transfer learning. Additionally, the utilization of pre-trained models addresses the challenge of the intricate design involved in the feature extraction component of deep networks that possess numerous hyperparameters. The suggested method has demonstrated significant potential for identifying CCPs, as evidenced by comparative experimental findings and a real-world case study.

Recycling plays a vital role in environmental sustainability and the conservation of natural resources. However, increasing population, industrialization, and waste have increased the need for automation in waste sorting processes. This study investigated the performance of deep learning-based classification models on waste classification using two different datasets (Garbage Classification and Garbage-Dataset). Experiments were conducted on four Convolutional Neural Networks (DenseNet121, InceptionV3, MobileNetV2, and VGG16), utilizing data augmentation and transfer learning techniques. A hybrid model was created by aggregating the features of these four models. Performance evaluation was performed with the 5-fold cross-validation method. In both datasets analyzed according to the experimental results, the hybrid model yielded the highest performance metrics, including Accuracy, Precision, Recall, F1-score, and ROC-AUC. A test accuracy rate of 85.88% was obtained in the Garbage Classification dataset and 91.26% in the Garbage-Dataset. The study highlights the critical impact of dataset size and model architecture choices on classification performance, providing an essential foundation for developing automation solutions in waste management.

Seeing through disguise: Getting to know you with a deep convolutional neural network

A review on modern and smart technologies for efficient waste disposal and management

Indonesia will become the third largest contributor of plastic waste in the world in 2024. This is due to the suboptimal management and recycling of waste. One way to reduce the accumulation of waste in the environment is through waste separation as the first step in recycling. In the field of informatics engineering, this process can be implemented using Convolutional Neural Network (CNN), a deep learning method designed to recognize and classify objects in digital images. This study aims to develop a high-accuracy CNN model for waste type classification using the TensorFlow framework. The analysis was carried out to determine the most appropriate CNN architecture in separating waste optimally. By implementing this algorithm, an automatic waste separation system can be built to support the efficiency of the recycling process. This research is expected to accelerate and simplify the waste separation process, while encouraging more effective waste management.

Traditional geological mapping methods, which rely on field observations and rock sample analysis, are inefficient for continuous spatial mapping of geological features such as alteration zones. Deep learning models such as convolutional neural networks (CNNs) have ushered in a transformative era in remote sensing data analysis. CNNs excel in automatically extracting features from image data for classification and regression problems. CNNs have the ability to pinpoint specific mineralogical changes attributed to mineralisation processes by discerning subtle features within remote sensing data. In this study, we deploy CNNs with three sets of remote sensing data, namely Landsat 8, Landsat 9, and ASTER, to delineate diverse alteration zones within a mineral-rich region north of Broken Hill in western New South Wales, Australia. Our methodology involves model training using two distinct sets of training samples generated through ground truth data and a fully automated approach through selective principal component analysis (PCA). We also compare CNNs with conventional machine learning models, including k‑nearest neighbours, support vector machines, and multilayer perceptron. Our findings indicate that training with a ground truth-based dataset produces more reliable alteration maps. Additionally, we find that CNNs perform slightly better when compared to conventional machine learning models, which further demonstrates the ability of CNNs to capture spatial patterns in remote sensing data effectively. We find that Landsat 9 surpasses Landsat 8 in mapping iron oxide areas when employing the CNNs model trained with ground truth data obtained by field surveys. We also observe that using ASTER data with the CNNs model trained on the ground truth-based dataset produces the most accurate maps for two other important types of alteration zones, argillic and propylitic. This underscores the utility of CNNs in enhancing the efficiency and precision of geological mapping, particularly in discerning subtle alterations indicative of mineralisation processes, especially those associated with critical metal resources.

With increasing global attention on environmental protection and carbon neutrality goals, waste management has become a crucial component of achieving sustainability. Traditional waste disposal methods, such as manual sorting, identification, and crushing, often suffer from low efficiency and inconsistency. This study proposes a waste management system that integrates Artificial Intelligence (AI) to enhance the accuracy and efficiency of waste treatment through automation. The proposed system addresses waste classification, recognition, and fragmentation, utilizing advanced AI technologies like Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), Visual Transformers (ViT), and Deep Reinforcement Learning (DRL). To optimize the waste management process, the study used the KTH-TIPS dataset, which contains 800 high-resolution images of various material types. Robust model training was ensured through a series of preprocessing and data augmentation techniques. The experimental results demonstrated that CNN and Transformer architectures, including MobileNet, ResNet, and ViT, achieved high accuracies of 100.00%, 100.00%, and 96.91%, respectively. While the GNN and VGG architectures had slightly lower accuracies of 82.88% and 84.57%, they still demonstrated competitive performance. The experimental results illustrate the variation in training and testing losses across different models over the training cycles, revealing the learning dynamics and efficiency of each model.

The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this review, Part 2, the use of CNNs for NIR fruit quality evaluation is explored, given the success of CNNs in various other fields, such as image, video, speech, and audio processing, and the availability of large (open source) datasets of fruit spectra and reference quality attribute, which is required for the training of CNN models. The review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling, with advantages and disadvantages identified. Studies using CNN for NIR based fruit quality evaluation are then critically examined. Eight publications have presented on models using the same open-source mango dry matter calibration and test set, enabling inter-method comparisons. CNN models have been demonstrated to be accurate, precise and robust. Techniques of transfer learning for CNN models offer an alternative solution to model updating and calibration transfer methods applied in traditional chemometrics. The review has highlighted crucial areas that require resolution and exploration in this application through future research, including, (i) data requirements for training a CNN (ii) optimal spectral pre-processing for CNN (iii) CNN architecture and hyper-parameter selection and tuning for fruit quality evaluation (iv) CNN model interpretability and explainability. Future studies must conduct clearer comparison to partial least squares (PLS) regression and shallow ANNs to better assess the prospective benefit of using CNN, a more complex model. The potential for visualisation of spectra relevance to the CNN model using techniques such as GradCam, currently employed in visualising 2D-CNN models, remains to be explored.