An automated solid waste bin level detection system using a gray level aura matrix.

An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions.

[1]  Hassan Basri,et al.  Integrated technologies for solid waste bin monitoring system , 2010, Environmental monitoring and assessment.

[2]  A. Giusti,et al.  Sensorized waste collection container for content estimation and collection optimization. , 2009, Waste management.

[3]  R. Pallàs-Areny,et al.  Capacitive level sensing for solid-waste collection , 2003, Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498).

[4]  Valerie M Thomas.  Product self-management: evolution in recycling and reuse. , 2003, Environmental science & technology.

[5]  Alessandro Giusti,et al.  Early detection and evaluation of waste through sensorized containers for a collection monitoring application. , 2009, Waste management.

[6]  Robina Goodlad.  Housing and local government , 2001 .

[7]  Rawshan Ara Begum,et al.  Attitude and behavioral factors in waste management in the construction industry of Malaysia , 2009 .

[8]  Ian D. Williams,et al.  ‘Carbon footprinting’: towards a universally accepted definition , 2011 .

[9]  Ibrahim M. Elfadel,et al.  Gibbs Random Fields, Cooccurrences, and Texture Modeling , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  M A Hannan,et al.  Solid waste monitoring system integration based on RFID, GPS and camera , 2010, 2010 International Conference on Intelligent and Advanced Systems.

[11]  Ola M Johansson,et al.  The effect of dynamic scheduling and routing in a solid waste management system. , 2006, Waste management.

[12]  Xuejie Qin,et al.  Aura 3D Textures , 2007, IEEE Transactions on Visualization and Computer Graphics.

[13]  Driss Ouazar,et al.  Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting , 2009, Expert Syst. Appl..

[14]  Mansoor Ali,et al.  Partnerships for solid waste management in developing countries: linking theories to realities , 2004 .

[15]  Wojciech M. Budzianowski,et al.  Sustainable biogas energy in Poland: Prospects and challenges , 2012 .

[16]  Ian D. Williams,et al.  Carbon footprinting for climate change management in cities , 2011 .

[17]  Shu Liao,et al.  Texture Classification by using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[18]  Georg Brasseur,et al.  Design and analysis of a capacitive moisture sensor for municipal solid waste , 2008 .

[19]  Lansford C. Bell,et al.  ATTRIBUTES OF MATERIALS MANAGEMENT SYSTEMS , 1986 .

[20]  Sankar K. Pal,et al.  Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces , 2010, IEEE Transactions on Knowledge and Data Engineering.

[21]  Hassan Basri,et al.  Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system. , 2011, Waste management.

[22]  Foo Tuan Seik.  Recycling of domestic waste: Early experiences in Singapore , 1997 .

[23]  Chien-Yu Huang,et al.  Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study , 2009, Expert Syst. Appl..

[24]  Xuejie Qin,et al.  Basic gray level aura matrices: theory and its application to texture synthesis , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.