Abstract
Because of the cost and complexity of implementing an optical paper sorting system, the demand for an intelligent system for waste paper sorting has increased. This research focused on the development of a smart intelligent system (SIS) for recyclable waste paper sorting. The basis for selecting the regions of interests (ROIs) is the margin area of a paper object image because almost all printed documents keep the margin area intact. The paper grade is identified using a proximity search. The SIS with the HSI colour space offered maximum success rates of 99 %, 82 % and 89 %, while with the RGB model, the classification success rates were 94 %, 93 % and 98 % for white paper, old newsprint paper and old corrugated cardboard, respectively. The SIS is clearly superior to other prevailing techniques because of the faster decision making and lower cost of implementation.
Zusammenfassung
Aufgrund der Kosten und der Komplexität der Implementierung eines optischen Papiersortiersystems hat die Nachfrage nach intelligenten Systemen für die Altpapiersortierung zugenommen. Das Forschungsprojekt konzentrierte sich auf die Entwicklung eines smarten, intelligenten Systems (SIS) für die Sortierung von recycelbarem Altpapier. Die Grundlage für die Auswahl der Regions of Interest (ROIs) liegt im Randbereich der Papierobjektbilder, da fast alle gedruckten Dokumente den Randbereich intakt lassen. Die Papiersorte wird mithilfe einer Nächste-Nachbarn-Klassifikation identifiziert. Unter Verwendung des HSI-Farbraums bot das SIS für bedrucktes weißes Papier, altes Zeitungspapier und alte Wellpappe maximale Klassifikationserfolgsraten von 99%, 82% und 89%. Bei Verwendung des RGB-Farbraums betrugen die Erfolgsraten 94%, 93% und 98%. Das SIS ist anderen gängigen Techniken aufgrund der schnelleren Entscheidungsfindung und der geringeren Implementierungskosten deutlich überlegen.
Funding source: Universiti Kebangsaan Malaysia
Award Identifier / Grant number: DIP-2018-020
Funding statement: The project is sponsored by the Universiti Kebangsaan Malaysia and DIP-2018-020.
About the authors
Professor Dr. Mohammad Osiur Rahman obtained PhD in smart vision sensing system from the Department of Electrical, Electronic and Systems Engineering from Universiti Kebangsaan Malaysia, Malaysia in 2012. He received his M.Sc.(Engg.) in Information and Communication Technology from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh in 2005 and B.Sc.(Engg.) in Electronics and Computer Science from Shah Jalal University of Science and Technology, Sylhet, Bangladesh in 1997. For basic contributions in the Mathematical, Statistics and Computer Sciences, he received “UGC Award 2015” from His Excellency Honourable President of the People’s Republic of Bangladesh on 2 Nov 2016 at Osmani Memorial Auditorium, Dhaka, Bangladesh. He is serving as a Professor in the Department of Computer Science and Engineering, University of Chittagong, Chattogram, Bangladesh. His research interests include Artificial Intelligence, Advanced Software Engineering, Computational Biology, Computer Vision Systems, Image Processing, Pattern Recognition, Expert Systems, Soft Computing, Real Time System Development, DNA Computing and ICT.
Professor Aini Hussain obtained her B. Sc. in Electrical Engineering from Louisiana State University (LSU), USA; M. Sc. in Systems & Control from University of Manchester Institute of Science and Technology (UMIST), UK and PhD in Electrical & Electronics from the National University of Malaysia (UKM), Malaysia. She is a professor at the Department of Electrical, Electronic, and Systems Engineering (EESE), UKM. She leads the Smart Engineering Systems Research Group (SESRG) of UKM. She was the Chair of the Center for Integrated Engineering Systems and Advanced Technologies (INTEGRA) from Jan. 2018 -Nov. 2019. Her main area of research is in Intelligent Engineering Systems which involves Pattern Recognition, Computer Vision, Machine learning, Computational Intelligence and Signal Processing of Image, Video, Speech, Bio-signals and Power Quality. Currently, her research interests also involve complex event processing and IoT for development of smart engineering system applications.
Prof. Hassan Basri is currently Professor of Environmental Engineering at Universiti Kebangsaan Malaysia (UKM), and a Board Member in the Board of Engineers Malaysia (BEM). Prof. Hassan obtained his B.Eng. (Civil Engineering) with Honours from Tasmania University in 1982 and M.Sc.(Eng.) with Distinction (Tropical Public Health Engineering) from Leeds University in 1988. In 1994 he completed his PhD research, also at Leeds, where he developed a software for the design of sanitary landfills incorporating artificial intelligence. Prof Hassan specialises in environmental applications of smart engineering systems and has a strong interest in engineering education research. His research projects include the development of an automated prototype with intelligent vision sensors for sorting recycled containers and remote waste bin monitoring and, as founder, the UKM Zero Waste Campus initiative – a cluster of action-research projects targeting elimination of all solid waste produced by the UKM Bangi campus. Prof Hassan has until now published over 100 research articles in journals, over 150 in conference proceedings, several books, jointly filed 4 patents, conducted 12 environmental consultancies, and delivered 24 keynote/invited speeches at national and international meetings.
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