Journal of the Japan society of photogrammetry and remote sensing
Online ISSN : 1883-9061
Print ISSN : 0285-5844
ISSN-L : 0285-5844
Original Papers
Research Concerning Recursive Active Learning for Segmentation of Automobile Parts
Koki NAKAHATAYuhei YAMAMOTORyuichi IMAIDaisuke KAMIYAShigenori TANAKAMasaya NAKAHARA
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JOURNAL FREE ACCESS

2023 Volume 62 Issue 1 Pages 4-21

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Abstract

In traffic census, it is expected to develop image processing technologies for counting number of passing automobiles by analyzing video image. Many counting technologies using deep learning have been proposed. It is difficult to maintain sufficient accuracy because new automobiles are sold year after year. Therefore, it is necessary to maintain high accuracy by re-learning training data of automobiles with new shapes and colors continuously. However, maintenance labor cost is huge because training data have to be created continuously. In this research, technique to recursive active learning for segmentation of automobile parts is proposed and clarified its usefulness.

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© 2023 Japan Society of Photogrammetry and Remote Sensing
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