An automated solid waste bin level detection system using Gabor wavelet filters and multi-layer perception |
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| Institution: | 1. Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Malaysia;2. Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia, Malaysia;3. Institute for Environment and Development, Universiti Kebangsaan Malaysia, Malaysia;1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Hobart, Tasmania, Australia;1. Institute of Physics, University of Silesia, 40-007 Katowice, Poland;2. Silesian Center for Education and Interdisciplinary Research, University of Silesia, 41-500 Chorzów, Poland;1. Agronomy Division, Research and Development Centre, EID Parrry (I) Ltd., Pettavaithalai, Tamil Nadu, India;2. Central Tuber Crops Research Institute, Thiruvanthapuram, Kerala, India;3. Centre for Soil and Crop Management Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India;1. University of Coimbra, CITTA, Department of Civil Engineering, Coimbra, Portugal;2. EIGeS – Research Centre in Industrial Engineering, Management and Sustainability, Faculty of Engineering, Universidade Lusófona, Lisboa, Portugal;3. Centre for Management Studies, Instituto Superior Técnico (CEG-IST), Universidade de Lisboa, Lisbon, Portugal |
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| Abstract: | This paper examines the application of advanced computer image processing techniques integrated with communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera for solving the problem of solid waste collection and automated bin level detection. A new method of bin level detection is proposed that uses a Gabor wavelet filter as a feature extractor. Taking advantage of the desirable characteristics of Gabor filters, such as mask size and frequency selectivity, we have designed four filters corresponding to different frequency values for the extraction of waste features from images of the bin. The feature vector output based on Gabor filters is used as the input of the classification algorithm, which is a feed-forward back propagation (BP) network. The effectiveness of the proposed method is demonstrated on a large number of images. Both the mask size and the frequency of the Gabor filter allow it to serve as a feature extractor, and the bin level detector with the Gabor wavelet filter in conjunction with the BP acts as a classifier to provide a robust solution for solid waste automated bin level detection, collection and management. |
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