Research Concerning Recursive Active Learning for Segmentation of Automobile Parts
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- NAKAHATA Koki
- 関西大学大学院総合情報学研究科
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- YAMAMOTO Yuhei
- 関西大学環境都市工学部
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- IMAI Ryuichi
- 法政大学デザイン工学部
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- KAMIYA Daisuke
- 琉球大学工学部
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- TANAKA Shigenori
- 関西大学総合情報学部
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- NAKAHARA Masaya
- 大阪電気通信大学総合情報学部
Bibliographic Information
- Other Title
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- 車両部位識別のための再帰的能動学習に関する研究
Abstract
<p>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.</p>
Journal
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- Journal of the Japan society of photogrammetry and remote sensing
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Journal of the Japan society of photogrammetry and remote sensing 62 (1), 4-21, 2023
Japan Society of Photogrammetry and Remote Sensing
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Details 詳細情報について
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- CRID
- 1390017843891427200
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- ISSN
- 18839061
- 02855844
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed