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http://dx.doi.org/10.9728/dcs.2018.19.6.1213

Object Detection based on Mask R-CNN from Infrared Camera  

Song, Hyun Chul (Center of Virtual Reality and Augmented Reality, Nam-Seoul University)
Knag, Min-Sik (Department of Industrial Management Engineering, Nam-Seoul University)
Kimg, Tae-Eun (Department of Multimedia, Nam-Seoul University)
Publication Information
Journal of Digital Contents Society / v.19, no.6, 2018 , pp. 1213-1218 More about this Journal
Abstract
Recently introduced Mask R - CNN presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation mask of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask R - CNN is an algorithm that extends Faster R - CNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. The mask R - CNN is added to the high - speed R - CNN which training is easy and fast to execute. Also, it is easy to generalize the mask R - CNN to other tasks. In this research, we propose an infrared image detection algorithm based on R - CNN and detect heating elements which can not be distinguished by RGB images. As a result of the experiment, a heat-generating object which can not be discriminated from Mask R-CNN was detected normally.
Keywords
Deep Learning; Infrared camera; Mask R-CNN; Image Specification; Object Detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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