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A Novel Approach for Sensor Fusion Object Detection in Waste Sorting: The Case of WEEE

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dc.contributor.authorNazeri, Ali
dc.contributor.authorPlociennik, Christiane
dc.contributor.authorVogelgesang, Malte
dc.contributor.authorLi, Chanchan
dc.contributor.authorRuskowski, Martin
dc.contributor.editorWohlgemuth, Volker
dc.contributor.editorKranzlmüller, Dieter
dc.contributor.editorHöb, Maximilian
dc.date.accessioned2023-12-15T09:22:23Z
dc.date.available2023-12-15T09:22:23Z
dc.date.issued2023
dc.description.abstractThis paper investigates the application of AI-based methods for characterizing waste materials in sorting processes. With the increasing use of sensors in waste sorting systems, there is an opportunity to integrate data and improve accuracy. AI methods, such as deep object detection models, have the potential to optimize waste management processes and promote sustainability. This research examines the utilization of Sensor Fusion Object Detection in a multi-sensor sorting system, focusing on two different data fusion methods: concatenation and image mirroring. In the first approach, image data is concatenated with data from a hyperspectral near-infrared camera (NIR) and an inductive sensor, where dimensionality reduction techniques are applied to the data from both sensors. The second approach relies on a specific combination of NIR and inductive sensor data to simulate the format of image data. A Siamese Object Detection architecture is developed to train the model. The real-world testing results show that both approaches improve waste characterization accuracy and reliability by augmenting the models’ mean average precision (mAP). These findings demonstrate the potential for AI-based methods to transform the waste separation and management process, leading to more sustainable practices and resource efficiency.en
dc.identifier.doi10.18420/env2023-016
dc.identifier.isbn978-3-88579-736-4
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43336
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofEnviroInfo 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-342
dc.subjectDeep Neural Networks
dc.subjectSensor-Based Sorting
dc.subjectE-Waste
dc.subjectSiamese Networks
dc.titleA Novel Approach for Sensor Fusion Object Detection in Waste Sorting: The Case of WEEEen
dc.typeText/Conference Paper
gi.citation.endPage186
gi.citation.publisherPlaceBonn
gi.citation.startPage177
gi.conference.date11.-13. Oktober 2023
gi.conference.locationGarching, Germany
gi.conference.reviewfull
gi.conference.sessiontitleEnvironmental Impact Assessment and Optimization

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P342 - EnviroInfo 2023
Environmental Informatics 2023