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Sensors and Materials, Volume 32, Number 1(2) (2020)
Copyright(C) MYU K.K.
pp. 159-170
S&M2093 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2577
Published: January 20, 2020

Using Fully Convolutional Networks for Floor Area Detection [PDF]

Cheng-Jian Lin, Yu-Chi Li, and Chin-Ling Lee

(Received July 29, 2019; Accepted October 8, 2019)

Keywords: image sensor, fully convolutional networks, floor area detection, fuzzy integral, image segmentation

Most mobile robots use visual images to obtain information about the surrounding environment and the nonlinear diffusion method to detect candidate areas of the floor, but they could not be applied to more complicated environments. In this study, a hybrid of fully convolutional networks (FCNs) and fuzzy integral is proposed for detecting the position of the floor and nonfloor from visual images. FCN is an end-to-end, pixels-to-pixels network for semantic segmentation. Semantic segmentation aims to perform dense segmentation tasks on images and segments each pixel to a specified category. To overcome the majority decision drawback in the traditional voting method and increase the accuracy, the fuzzy integral is used for the fusion of multiple FCNs with various optimal methods. The overall accuracy, mean accuracy, and mean intersection over union (MIoU) of the proposed method are 0.9824, 0.9816, and 0.9577, respectively. The experimental results show that the proposed hybrid method has better accuracy than other methods in identifying the location of the floor area.

Corresponding author: Cheng-Jian Lin


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Cite this article
Cheng-Jian Lin, Yu-Chi Li, and Chin-Ling Lee, Using Fully Convolutional Networks for Floor Area Detection, Sens. Mater., Vol. 32, No. 1, 2020, p. 159-170.



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