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A Machine Vision Model for Solid Waste Classification using Smart Intelligence

Xiaolan Zeng, Sulala M. Z. F. Al-Hamadani


The waste sorting is carried out using machines still require high precision is discriminating objects into appropriate categories. The waste objects are fed in a conveyor where the objects are separated manually and by tools. The cost of separating manually becomes high and time consuming with humans and the performance of separating tools depends on the precision of classifying the objects automatically. The classification of each object is presently aided through computer vision and machine learning technology. Data mining is a process of extracting useful information and patterns hidden inside large volume of data. Machine learning is a division of data mining through which algorithms gain the ability to improve their performance through self-learning. Data mining and machine learning algorithms are used in variety of data formats which includes numerical data, signals, multimedia formats, spatial data, sensor data. In this paper a novel idea for the solid waste classifications are given along with the future enhancements.


Digital Image Processing, Solid Waste Classification, Machine Learning.

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