SURVEY ON DEEP LEARNING UTILITY FOR THE PURPOSE OF GARBAGE CATEGORIZATION

Abstract

The increasing pace of solid waste production in the world has led to the overload of the municipal management systems, revealing the weakness of the manual sorting and manual automation of the robotic sorting of the heterogeneous waste under the conditions of variations. Convolutional Neural Network (CNN) and deep learning (DL) provide automated algorithms that enhance speed of classification, accuracy, and performance. The current trends combine real-time object detectors with multi-layered CNNs to deal with class imbalance, overfitting, and a lack of data diversity, and lightweight models and transfer learning permit deployment in low-resource IoT settings. Hybrid classification-detection algorithms improve model stability under cluttered environment, and UAVs-based surveillance is useful in fast mapping of hazardous waste piles. Applications of DL are all around urban streets, underwater debris tracking and construction site trash sorting, and natural language interfaces. Circular economy goals are also promoted by these technologies and allow recycling and resource recovery to be more efficient. However, there are still shortcomings, such as inadequate datasets, large computation energy demands, and realistic multi-scale implementation. Future directions to enhance the model architecture, use less energy in the training process and develop hardware-software architecture to implement it at large scale and in a sustainable manner should be considered in future research. On the whole, DL is revolutionizing waste management to enable the classification and monitoring of waste as accurate, automated and environmentally friendly, and make smart waste systems one of the most important elements of an urban infrastructure that is sustainable.

KEYWORDS

Categorization, Convolutional Neural Networks (CNNs), Deep Learning (DL), Internet of Things (IoT), Lightweight models, Transfer learning, Waste, Waste management