论文标题
关于使用短波 - 内红外超光谱成像的预处理和模型复杂性的塑性分析的效果
On the Effect of Pre-Processing and Model Complexity for Plastic Analysis Using Short-Wave-Infrared Hyper-Spectral Imaging
论文作者
论文摘要
塑料废物回收的重要性是不可否认的。在这方面，计算机视觉和深度学习通过对塑料的短波 - 内红外超光谱图像的自动分析来启用解决方案。在本文中，我们提供了一项详尽的经验研究，以表明使用深度学习对各种塑料薄片进行超光谱图像分割的任务的重要性。我们评估通用和专业模型的复杂性水平，并推断出它们的性能能力：通用模型通常不必要地复杂。我们介绍了一种专业的超光谱架构塑料网的两个变体，这些变体在性能和计算复杂性中都优于几个知名的分割体系结构。此外，我们阐明了信号预处理在超光谱成像领域内的重要性。为了完成我们的贡献，我们介绍了四种主要聚合物类型的塑料片的最大，最通用的超光谱数据集。
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.
