Bin Level Detection Using Gray Level Co-occurrence Matrix in Solid Waste Collection

This paper presents the image processing technique gray level co-occurance matrix (GLCM) in solid waste bin level detection and classification. Advanced communication technologies are integrated with GLCM to improve the waste collection operation. The GLCM parameters such as displacement (d) and quantization (G) are investigated to determine the best parameters values of the bin images. The optimum classification accuracy of the system is obtained by investigating the values of d and G. In this paper, the parameters values with selected texture features are used to form the GLCM database. The most appropriate features collected from the GLCM are then used as inputs to the multi-layer perception (MLP) and K-nearest neighbor (KNN) for bin image classification and grading. The results demonstrated that the KNN classifier at KNN=3, d=1 and maximum G values performs better than that of using MLP with same database. Based on the results, this new method has the potential to be used in solid waste bin level classification and grading to provide a robust solution for solid waste bin level detection, collection, monitoring and management.