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Smart city article details

Title	Trashintelai- Garbage Collection Automation Using Deep Learning And Image Processing
ID_Doc	58937
Authors	Jadhav R.; Mhaske A.; Nawale A.; Oswal M.; Mulhar S.
Year	2023
Published	2023 Global Conference on Information Technologies and Communications, GCITC 2023
DOI	http://dx.doi.org/10.1109/GCITC60406.2023.10426067
Abstract	Garbage collection in urban areas has become a major challenge due to the increase in trash production. New technologies, including the application of deep learning and image processing methods, have been created to solve this problem. In this study, we present an automated garbage collection system that recognizes and categorizes rubbish in real-time using computer vision and machine learning methods. A deep learning model classifies the garbage into several categories, and the system also contains an image processing pipeline that pre-processes incoming photos and extracts relevant data. We executed experiments on a real-world dataset to assess the performance of the suggested solution. The outcomes demonstrate that the suggested approach classifies garbage with a high degree of accuracy. The system can recognize and categorize various waste categories, including recyclables, organic garbage, and non-recyclables, which can then be collected individually for proper disposal. This is made possible by the application of deep learning and image processing algorithms. The suggested approach can potentially increase trash disposal efficiency, reduce waste sent to landfills, and promote a cleaner and healthier environment in metropolitan settings. The burden of garbage collection staff can be reduced, and the danger of harm can be reduced, with real-time monitoring and automated waste collection. © 2023 IEEE.
Author Keywords	Deep learning; image processing; mobile application; object detection; smart city; waste management

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