On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging

• URL: http://arxiv.org/abs/2203.11209v1
• Date: Mon, 21 Mar 2022 11:19:11 GMT
• Title: On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging
• Authors: Klaas Dijkstra, Maya Aghaei, Femke Jaarsma, Martin Dijkstra, Rudy Folkersma, Jan Jager, Jaap van de Loosdrecht
• Abstract summary: Computer vision and deep learning enable automated analysis of short-wave-infrared hyper-spectral images of plastics. In this paper, we show the importance of efficient model selection for resolving the task of hyper-spectral image segmentation. We introduce the largest, most versatile hyper-spectral dataset of plastic flakes of four primary polymer types.
• Score: 0.11083289076967892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The importance of plastic waste recycling is undeniable. In this respect, computer vision and deep learning enable solutions through the automated analysis of short-wave-infrared hyper-spectral images of plastics. In this paper, we offer an exhaustive empirical study to show the importance of efficient model selection for resolving the task of hyper-spectral image segmentation of various plastic flakes using deep learning. We assess the complexity level of generic and specialized models and infer their performance capacity: generic models are often unnecessarily complex. We introduce two variants of a specialized hyper-spectral architecture, PlasticNet, that outperforms several well-known segmentation architectures in both performance as well as computational complexity. In addition, we shed lights on the significance of signal pre-processing within the realm of hyper-spectral imaging. To complete our contribution, we introduce the largest, most versatile hyper-spectral dataset of plastic flakes of four primary polymer types.

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