Classification of Metal Objects Using Deep Neural Networks in Waste Processing Line (original) (raw)

Intelligent and Real-Time Detection and Classification Algorithm for Recycled Materials Using Convolutional Neural Networks

Recycling, 2022

In recent years, the production of municipal solid waste has constantly been increasing. Recycling is becoming more and more important, as it is the only way that we can have a clean and sustainable environment. Recycling, however, is a process that is not fully automated; large volumes of waste materials need to be processed manually. New and novel techniques have to be implemented in order to manage the increased volume of waste materials at recycling factories. In this paper, we propose a novel methodology that can identify common waste materials as they are being processed on a moving belt in waste collection facilities. An efficient waste material detection and classification system is proposed, which can be used in real integrated solid waste management systems. This system is based on a convolutional neural network and is trained using a custom dataset of images, taken on site from actual moving belts in waste collection facilities. The experimental results indicate that the ...

Comparative Analysis of Multiple Deep CNN Models for Waste Classification

ArXiv, 2020

Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of dustbin is used to segregate those wastes into different compartments. Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management. Municipal solid waste is a huge, renewable source of energy. The situation is win-win for both government, society and industrialists. Because of fine-tuni...

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%.

Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models

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...

An Innovative Automated Robotic System based on Deep Learning Approach for Recycling Objects

Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics, 2019

In this paper, an industrial robotic recycling system that is able to grasp objects and sort them according to their materials is presented. The system architecture is composed of a robot manipulator with a multifunctional grasping tool, one platform, a depth and an RGB camera. The innovation of this work consists of integrating image processing, grasping, motion planning and object material classification to create a new automated recycling system framework. An efficient object recognition approach is presented that uses segmentation and finds grasping points to properly manipulate objects. A deep learning approach was also used with a modified LeNet model for waste objects classification, sorting them into two main classes: carton and plastic. Image processing and classification were integrated with motion planning that is used to move the robot with optimized trajectories. To evaluate the system, the success rate and the execution time for grasping and object classification were computed. In addition, the accuracy of the network model was evaluated. A total success rate of 86.09% and 90% was obtained for carton and plastic samples grasped using suction, while 86.67% and 78.57% using gripper. In addition, a classification accuracy of 96% was reached on test samples

Hybrid Approach of Garbage Classification Using Computer Vision and Deep Learning

International Journal of Engineering Applied Sciences and Technology

As waste segregation becomes an important issue in our lives, with the use of technology like deep neural networks and computer vision, we can make the process efficient and robust by image segmentation and classification. These systems on the rise need accurate and efficient segmentation and recognition mechanisms and this demand coincides with the increase of computational capabilities of modern computer architectures and more effective algorithms for image recognition. This paper does a comparative analysis of various different approaches and methods like Simple CNN, ResNet50, VGG16, etc in brief. The comparative analysis and study explains the performance of every approach, this paper concludes that ResNet50 gives excellent performance. VGG16 network also provides good performance which meets the needs of daily use.

Waste Classification using Convolutional Neural Network

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Garbage is one of the most dangours product present onto earth and infact in this thenon-degradable part is veritably dangerous from mortal as well as beast point ofview.So we aimed to make a design which can sagrigate the type of scrap as per its parcels like paper, cupboard, glass, plastic etc. The thing of this design is to contribute to the development and enhancement of machine literacy ways concentrated on working environmental issues like the waste accumulation, global warming, pollution, etc. likewise, it'll be subject to trial by professionals and subject matter experts since its open-source. That trial is crucial when perfecting the performance of models in the field of machine literacy, along with the data gathering and processing ways used in the process.

A Survey on Waste Detection and Classification using Deep Learning

IEEE Access, 2022

Waste or trash management is receiving increased attention for intelligent and sustainable development, particularly in developed and developing countries. The waste or trash management system comprises several related processes that carry out various complex functions. Recently, interest in deep learning (DL) has increased in providing alternative computational techniques for determining the solution to various waste or trash management problems. Researchers have concentrated on this domain, and as a result, significant research has been published, particularly in recent years. According to the literature, a few comprehensive surveys have been done on waste detection and classification. However, no study has investigated the application of DL to solve waste or trash management problems in various domains and highlight the available datasets for waste detection and classification in different domains. To this end, this survey contributes by reviewing various image classification and object detection models, and their applications in waste detection and classification problems, providing an analysis of waste detection and classification techniques with precise and organized representation and compiling over twenty benchmarked trash datasets. Also, we backed up the study with the challenges of existing methods and the future potential in this field. This will give researchers in this area a solid background and knowledge of the state-of-the-art deep learning models and insight into the research areas that can still be explored.

ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning

arXiv (Cornell University), 2023

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model.

Waste Management Using Machine Learning and Deep Learning Algorithms

2020

Waste management is one of the essential issues that the world is currently facing, and it does not matter if the country is developed or underdeveloped. The key issue in this waste segregation is that the trash bin at open spots gets flooded well ahead of time before the beginning of the cleaning process. The cleaning process involves with the isolation of waste that could be due to unskilled workers, which is less effective, time-consuming, and not plausible because the reality is, there is a lot of waste. So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. The goal of this task is to gather a dataset and arrange it into six classes consisting of glass, paper, metal, plastic, cardboard, and waste. The model that we have used are the classification models. For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. For our model, we found CNN accomplished high characterization on classification accuracy, which is around 90%, while SVM indicated an excellent transformation to various kinds of waste, with 85% classification accuracy, and Random Forest and Decision Tree have accomplished 55% and 65% classification accuracy respectively.