Next-Generation Recycling: Automated Waste Sorting Using Object Detection and Classification Models
DOI:
https://doi.org/10.32628/CSEIT25111716Keywords:
Classification, Deep learning, VGG 16, Waste management, Waste sortingAbstract
In today’s world, appropriate waste sorting is a crucial aspect of effective waste management which is essential to the sustainability of the environment. Manual labour is a significant aspect of conventional waste sorting processes, which very often leads to human errors, inefficiencies, and is expensive to implement. With the rapid increase in waste generation, there is an increasing need for smarter and more reliable sorting systems. This project outlines a smart waste sorting system with the ability to automatically identify and sort waste items into the following categories: paper, plastic, glass, metal, and organic waste, using deep learning, specifically the VGG16 neural network. The system uses a well-balanced dataset made up of various waste items images to train the model, and makes use of image pre-processing, and augmentation to improve the weight. The system also integrates the YOLO (You Only Look Once) deep learning algorithm in order to create a real time sorting s ystem for improved functionality for practice. The collaborative use of VGG16 and YOLO not only increases accuracy, but also ensures speed and reliability. The general aim of the approach is to reduce human resources and sorting errors and promote more environmentally responsible waste disposal.
Downloads
References
Hossen, Md Mosarrof, et al. "A reliable and robust deep learning model for effective recyclable waste classification." IEEE Access 12 (2024): 13809-13821. DOI: https://doi.org/10.1109/ACCESS.2024.3354774
Alsabt, Reema, et al. "Optimizing waste management strategies through artificial intelligence and machine learning-An economic and environmental impact study." Cleaner Waste Systems 8 (2024): 100158. DOI: https://doi.org/10.1016/j.clwas.2024.100158
Tao, Junyu, et al. "Combination of hyperspectral imaging and machine learning models for fast characterization and classification of municipal solid waste." Resources, Conservation and Recycling 188 (2023): 106731. DOI: https://doi.org/10.1016/j.resconrec.2022.106731
Cheema, Sehrish Munawar, Abdul Hannan, and Ivan Miguel Pires. "Smart waste management and classification systems using cutting edge approach." Sustainability 14.16 (2022): 10226. DOI: https://doi.org/10.3390/su141610226
Ahmed, Mohammed Imran Basheer, et al. "Deep learning approach to recyclable products classification: Towards sustainable waste management." Sustainability 15.14 (2023): 11138. DOI: https://doi.org/10.3390/su151411138
Nežerka, V., T. Zbíral, and Jan Trejbal. "Machine-learning-assisted classification of construction and demolition waste fragments using computer vision: Convolution versus extraction of selected features." Expert Systems with Applications 238 (2024): 121568.. DOI: https://doi.org/10.1016/j.eswa.2023.121568
Choi, Janghee, Byeongju Lim, and Youngjun Yoo. "Advancing plastic waste classification and recycling efficiency: Integrating image sensors and deep learning algorithms." Applied Sciences 13.18 (2023): 10224. DOI: https://doi.org/10.3390/app131810224
Mohammed, Mazin Abed, et al. "Automated waste-sorting and recycling classification using artificial neural network and features fusion: A digital-enabled circular economy vision for smart cities." Multimedia tools and applications 82.25 (2023): 39617-39632. DOI: https://doi.org/10.1007/s11042-021-11537-0
Malik, Meena, et al. "Waste classification for sustainable development using image recognition with deep learning neural network models." Sustainability 14.12 (2022): 7222. DOI: https://doi.org/10.3390/su14127222
Ming, Li Wei, et al. "Ai as a driver of efficiency in waste management and resource recovery." International Transactions on Artificial Intelligence 2.2 (2024): 128-134. DOI: https://doi.org/10.33050/italic.v2i2.547
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
