Spectral properties inspired image enhancement for crystalline polymer powder impurity detection

Crystalline polymer powder inevitably incorporates certain impurities, including decomposed polymers or foreign particles. An essential criterion for assessing the quality of polymers is the quantity of contaminants in the powder. However, it is challenging to discern powder contaminants through machine vision due to the poor quality of images taken at production sites. Inspired by the spectral properties of crystalline polymers, this paper proposes an efficient image-based impurity detection method, which seeks to precisely and robustly detect contaminant content. Based on the changes in absorbance during polymer decomposition, a highly selective channel-weighted image enhancement approach is designed to emphasize the difference between impurities and normal particles. Then, using the prior information on the powder's attributes, an adaptive thresholding method is employed to categorize pixels belonging to impurities. Finally, a dataset of 119 12-megapixel photos from a chemical facility, where the average size of contaminants in images is 43 pixels, is used to evaluate the performance of the proposed algorithm. The results of the detection demonstrate that the proposed strategy for image enhancement has better selectivity to impurities than typical image enhancement methods.