Pellet fuels are nowadays commonly used as a heat source for food preparation. Unfortunately, they may contain intrusions which might be harmful for humans and the environment. The intrusions can be identified precisely using immersed microscopy analysis. The aim of this study is to investigate the possibility of autonomous identification of selected classes of intrusions using relatively simple deep learning models. The semantic segmentation was chosen as a method for impurity identification in the microscopic image. Three architectures of deep networks based on UNet architecture were examined. The networks contained the same depth as UNet but with a successively limited number of filters. The input image influence on the segmentation results was also examined. The efficiency of the network was assessed using the intersection over union index. The results showed an easily observable impact of the filter used on segmentation efficiency. The influence of the input image resolution is not so clear, and even the lowest (256 × 256 pixels) resolution used gave satisfactory results. The biggest (but still smaller than originally proposed UNet) network yielded segmentation quality good enough for practical applications. The simpler one was also applicable, although the quality of the segmentation decreased considerably. The simplest network gave poor results and is not suitable in applications. The two proposed networks can be used as a support for domain experts in practical applications.
Keywords: computer vision; deep learning; image analysis; intrusion detection; microscopy; petrography.