An approach of classifying waste using transfer learning method

One of the most critical issues facing the world is waste management, regardless of whether the region is being established or becoming established. There is a waste partitioning process in waste management, and the main challenge is that the garbage space is flooded long before the beginning of the following cleaning process at clear spots. Only unskilled workers conduct waste separation, which is less accurate, time-consuming, and not utterly possible due to the enormous amount of waste. Using the Convolutional Neural Network, we propose an artificial waste classification problem to compile and organize a dataset into seven categories consisting of metal, plastic, glass, paper, cardboard, trash, and E-waste. We then distinguished between specific transfer learning algorithms for our project: Xception, DenseNet121, Resnet-50, MobilenetV2, and EffiecienNetB7. DenseNet121 achieved a high precision characterization of about 93.3% for our model, while Mobilenet also demonstrated an incredible conversion to different forms of waste of 93% and Resnet-50, Xception and EfffiecienNetB7 achieved 92%, 92.5%, and 87%, respectively. In the future, we would like to increase the accuracy by using some other hyperparameter tuning, and we would like to deploy the project on mobile devices. We will use dockers or Kubernetes to deploy and YOLO real-time object detection as a framework for the post

Waste management using machine learning and deep learning algorithms

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