A Survey on Waste Detection and Classification Using Deep Learning

Abstract

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.

Musical instrument classifier for early childhood percussion instruments

“…The model architecture proposed by Blazke and Kostek also makes it possible to add new instruments [ 50 ]. Some challenges to using DNNs include the large datasets needed for training and computational costs [ 51 ].…”

Detection of recyclable solid waste using convolutional neural networks and PyTorch

“…La detección de residuos mediante visión por computadora emplea algoritmos de aprendizaje automático para reconocer y clasificar diferentes tipos de residuos. Esta técnica implica el uso de cámaras y sensores para capturar imágenes de los residuos, seguido por el análisis de estas imágenes a través de software especializado que utiliza técnicas de visión por computadora y aprendizaje automático [5]. Los algoritmos, previamente entrenados con un conjunto de datos que incluye diversas imágenes de residuos, logran una detección y clasificación precisa [6].…”

Classification of waste in natural environments

“…Kaya introduces a new model of waste classification for sustainable development which involves the use of a smart garbage system [3]. Abdu and Mohd Noor provide an extensive survey on waste detection and classification focusing on deep learning techniques [4]. Ruiz et al present an image based automatic waste classification approach that combines machine learning with waste management practices [5].…”