Convolutional Neural Networks have become an important tool for various Computer Vision tasks. Yet, increasing complexity of such architectures drives computational costs. To this end, we propose two measures to achieve similar classification results as state-of-the-art architectures while at the same time reducing model complexity significantly. Firstly, we describe a novel type of non-linear parameter-efficient morphological layers inspired by concepts that are well-known and widely used with convolutions. Secondly, we present a set of simple network architectures, organized as optimization framework, which is enhanced by neural architecture search and hyperparameter optimization. In experiments with hyperspectral remote sensing data, we demonstrate that the identified optimal morphological architecture produces results not only comparable with other architectures from the optimization framework, but also comparable or better than selected state-of-the-art neural network architectures for image classification. Depending on the performed task, the proposed optimized architecture requires up to 25 times fewer parameters than actual state-of-the-art networks.
The purity analysis of oilseed rape (Brassica napus L.) is currently a labor-intensive and manual process, requiring significant human effort for accurate assessment. In this context, the KIRa-Sorter system presents an innovative solution that leverages hyperspectral imaging technology for automating the comprehensive classification of various contaminants present in rapeseed samples. The initial phase of the KIRa-Sorter system involves the efficient capture of hyperspectral and RGB image data from rapeseed samples as input for classification. From up to 200 different types of foreign objects typically found in these samples, a reduced coreset has been defined that the system is able to automatically singulate, classify and physically sort.
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