Nazeri, AliPlociennik, ChristianeVogelgesang, MalteLi, ChanchanRuskowski, MartinWohlgemuth, VolkerKranzlmüller, DieterHöb, Maximilian2023-12-152023-12-152023978-3-88579-736-4https://dl.gi.de/handle/20.500.12116/43336This 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.enDeep Neural NetworksSensor-Based SortingE-WasteSiamese NetworksText/Conference Paper10.18420/env2023-0161617-5468