Skinny: A Lightweight U-Net For Skin Detection And Segmentation
Tomasz Tarasiewicz, Jakub Nalepa, Michał Kawulok
Abstract
The use of deep-learned features has recently allowed for improving the performance of skin detection and segmentation. In particular, fully-convolutional U-Nets, proposed for segmenting medical images, occurred to be extremely effective here. However, the spatial context, which is rather narrow for U-Nets, may be more important for skin segmentation than for segmenting other image structures. We propose Skinny-a lightweight U-Net-based architecture that extends the range of multi-scale analysis. The results of our experiments indicate that Skinny outperforms the state-of-the-art skin segmentation techniques, rendering the F-score of 92.3% and 94.9% for the ECU and HGR datasets, respectively.
Topics & Concepts
SegmentationArtificial intelligenceComputer scienceImage segmentationRendering (computer graphics)Market segmentationComputer visionContext (archaeology)Convolutional neural networkScale-space segmentationPattern recognition (psychology)GeographyBusinessMarketingArchaeologyVideo Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect Detection