Real-Time Hair Segmentation Using Mobile-Unet

Electronics

Jang-Hee Yoo¹  Ho-Sub Yoon¹  Seong-Woo Park² 

[1] HRI Section, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajung-ro, Yueseong-gu, Daejeon 34129, Korea;ICT Department, ETRI School, University of Science & Technology, Daejeon 34129, Korea;

关键词: computer vision;    hair segmentation;    FCN;    deep learning;    Mobile-Unet;    HRI;   

DOI  :  10.3390/electronics10020099

来源: DOAJ

【 摘 要 】

We described a real-time hair segmentation method based on a fully convolutional network with the basic structure of an encoder–decoder. In one of the traditional computer vision techniques for hair segmentation, the mean shift and watershed methodologies suffer from inaccuracy and slow execution due to multi-step, complex image processing. It is also difficult to execute the process in real-time unless an optimization technique is applied to the partition. To solve this problem, we exploited Mobile-Unet using the U-Net segmentation model, which incorporates the optimization techniques of MobileNetV2. In experiments, hair segmentation accuracy was evaluated by different genders and races, and the average accuracy was 89.9%. By comparing the accuracy and execution speed of our model with those of other models in related studies, we confirmed that the proposed model achieved the same or better performance. As such, the results of hair segmentation can obtain hair information (style, color, length), which has a significant impact on human-robot interaction with people.

【 授权许可】

Unknown