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http://dx.doi.org/10.9766/KIMST.2020.23.2.139

Performance Improvement of a Deep Learning-based Object Recognition using Imitated Red-green Color Blindness of Camouflaged Soldier Images  

Choi, Keun Ha (Artificial Intelligence Research & Development Center, Army Training and Doctrine Command)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.23, no.2, 2020 , pp. 139-146 More about this Journal
Abstract
The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning. In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people. Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.
Keywords
Deep Learning; Camouflaged Soldier Recognition; Ensemble Learning; Red-green Color Blindness;
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