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The importance of plastic waste recycling is undeniable. In this respect,computer vision and deep learning enable solutions through the automatedanalysis of short-wave-infrared hyper-spectral images of plastics. In thispaper, we offer an exhaustive empirical study to show the importance ofefficient model selection for resolving the task of hyper-spectral imagesegmentation of various plastic flakes using deep learning. We assess thecomplexity level of generic and specialized models and infer their performancecapacity: generic models are often unnecessarily complex. We introduce twovariants of a specialized hyper-spectral architecture, PlasticNet, thatoutperforms several well-known segmentation architectures in both performanceas well as computational complexity. In addition, we shed lights on thesignificance of signal pre-processing within the realm of hyper-spectralimaging. To complete our contribution, we introduce the largest, most versatilehyper-spectral dataset of plastic flakes of four primary polymer types.

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