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A weakly supervised approach for recycling code recognition

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Titel: A weakly supervised approach for recycling code recognition
Autoren: Pellegrini Lorenzo, Maltoni Davide, Graffieti Graffieti, Lomonaco Vincenzo, Mazzini Lisa, Mondardini Marco, Zappoli Milena
Weitere Verfasser: Pellegrini Lorenzo, Maltoni Davide, Graffieti Graffieti, Lomonaco Vincenzo, Mazzini Lisa, Mondardini Marco, Zappoli Milena
Publikationsjahr: 2023
Bestand: IRIS Università degli Studi di Bologna (CRIS - Current Research Information System)
Schlagwörter: Recycling code recognition, Recycling symbols recognition, Waste recognition, Weakly supervised classification
Beschreibung: Waste sorting at the household level is a virtuous process that can greatly increase material recycling and boost the circular economy. To this purpose, waste must be differentiated by material (e.g., PVC, Polyethylene, Paper, Glass, Aluminum, etc.), a task that can be simplified by printing a recycling code on the product case. Unfortunately, the large number of recycling codes printed on products makes this process unfriendly for many users. In this work, we propose a vision-based mobile application to support users in recognizing recycling codes for proper waste sorting. The proposed system combines a dual-head CNN with an image processing pipeline (based on domain knowledge) in order to improve: (i) the reliability of symbol detection/classification and (ii) the weakly-supervised labeling of new samples during iterative training. Our experimental results prove the feasibility of developing effective applications with minimum effort in terms of data collection and labeling, which is one of the main obstacles to successfully applying deep-learning techniques to real-world problems.
Publikationsart: article in journal/newspaper
Dateibeschreibung: ELETTRONICO
Sprache: English
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000906892100009; volume:215; firstpage:1; lastpage:11; numberofpages:11; journal:EXPERT SYSTEMS WITH APPLICATIONS; https://hdl.handle.net/11585/912610; https://www.sciencedirect.com/science/article/pii/S0957417422023004
DOI: 10.1016/j.eswa.2022.119282
Verfügbarkeit: https://hdl.handle.net/11585/912610
https://doi.org/10.1016/j.eswa.2022.119282
https://www.sciencedirect.com/science/article/pii/S0957417422023004
Rights: info:eu-repo/semantics/openAccess
Dokumentencode: edsbas.10F751AC
Datenbank: BASE
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  Data: A weakly supervised approach for recycling code recognition
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  Data: <searchLink fieldCode="AR" term="%22Pellegrini+Lorenzo%22">Pellegrini Lorenzo</searchLink><br /><searchLink fieldCode="AR" term="%22Maltoni+Davide%22">Maltoni Davide</searchLink><br /><searchLink fieldCode="AR" term="%22Graffieti+Graffieti%22">Graffieti Graffieti</searchLink><br /><searchLink fieldCode="AR" term="%22Lomonaco+Vincenzo%22">Lomonaco Vincenzo</searchLink><br /><searchLink fieldCode="AR" term="%22Mazzini+Lisa%22">Mazzini Lisa</searchLink><br /><searchLink fieldCode="AR" term="%22Mondardini+Marco%22">Mondardini Marco</searchLink><br /><searchLink fieldCode="AR" term="%22Zappoli+Milena%22">Zappoli Milena</searchLink>
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  Data: Pellegrini Lorenzo<br />Maltoni Davide<br />Graffieti Graffieti<br />Lomonaco Vincenzo<br />Mazzini Lisa<br />Mondardini Marco<br />Zappoli Milena
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  Data: 2023
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  Data: <searchLink fieldCode="DE" term="%22Recycling+code+recognition%22">Recycling code recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Recycling+symbols+recognition%22">Recycling symbols recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Waste+recognition%22">Waste recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Weakly+supervised+classification%22">Weakly supervised classification</searchLink>
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  Data: Waste sorting at the household level is a virtuous process that can greatly increase material recycling and boost the circular economy. To this purpose, waste must be differentiated by material (e.g., PVC, Polyethylene, Paper, Glass, Aluminum, etc.), a task that can be simplified by printing a recycling code on the product case. Unfortunately, the large number of recycling codes printed on products makes this process unfriendly for many users. In this work, we propose a vision-based mobile application to support users in recognizing recycling codes for proper waste sorting. The proposed system combines a dual-head CNN with an image processing pipeline (based on domain knowledge) in order to improve: (i) the reliability of symbol detection/classification and (ii) the weakly-supervised labeling of new samples during iterative training. Our experimental results prove the feasibility of developing effective applications with minimum effort in terms of data collection and labeling, which is one of the main obstacles to successfully applying deep-learning techniques to real-world problems.
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  Data: 10.1016/j.eswa.2022.119282
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