Ma, Wing Shiu, Yuen, Shing Hang, Keung, Chi Nam, Qureshi, Mehak Fatima, Qureshi, Umair Mujtaba, and Umair, Zuneera (2024) Investigating Object Sorting Problem for Smart Garbage Application. In: Proceedings of the IEEE International Conference on Advancements in Computational Sciences. From: ICACS 2024: IEEE 5th International Conference on Advancements in Computational Sciences, 19-20 February 2024, Lahore, Pakistan.

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Abstract

In recent years, the rapid growth in industrialization is observed to promote the economic growth of nations. This indeed has raised the financial standards and ways of living from sustainability to a well-off living standards, but has counter effects on environmental conditions such as increasing waste generation, pollution etc, which has become a serious concern and can be alarming situation in nearby future. Thus, this research aims to create a sustainable solution to reduce the recycling material using the advance AI deep learning technology. In this research, we have designed and developed a smart recycling garbage bin that can effectively sort different types of recycling waste i.e. glass, paper, metal and plastic. To develop the smart garbage bin, the pre-trained Tensorflow model is utilized, after evaluating its performance and comparative analyses with YOLO model on the customize dataset. The final results shows that the model classified all the garbage images from the garbage used to test the smart garbage bin and achieved the high overall accuracy of 91%.

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