Learning Image-Based Contaminant Detection in Wool Fleece from Noisy Annotations

Springer International Publishing
Publication Type:
Computer Vision Systems, 2021, 12899 LNCS, pp. 234-244
Issue Date:
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This paper addresses the problem of detecting natural contaminants in freshly shorn wool fleece in RGB images using deep learning-based semantic segmentation. The challenge of inconsistent annotation is overcome by learning the probability of contamination as opposed to a discrete class. From the continuous value predictions, contaminated regions can be extracted by selectively thresholding on the probability of contamination. Furthermore, the imbalance of the class distributions is accounted for by adaptively weighting each pixel’s contribution to the loss function. Results show that the adaptive weight improves the prediction accuracy and overall outperforms learning an approximated representation by quantising the distributions.
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