| Presentation | 2023-03-03 Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data Pei-Chun Chien, Powei Liao, Eiji Fukuzawa, Jun Ohya, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Cable tendency is the potential shape or characteristic that a cable may possess while being manipulated during automated production, of which some are considered erroneous and should be identified as a part of anomaly detection. This research explores the ability of deep learning models in learning the cable tendencies that, contrary to typical classification tasks of multi-object scenarios, is to differentiate the multiple states displayable by the same object -- in this case, cables. By training multiple models with different combinations of self-collected real-world data and self-generated simulation data, a comparative study is carried out to compare the performance of each approach. In conclusion, the effectiveness of detecting five abnormal states and shapes of cables, and using simulation data is certificated in experiments. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | deep learningtendencyanomaly detectionsyntheticsimulation dataBlender |
| Paper # | PRMU2022-117,IBISML2022-124 |
| Date of Issue | 2023-02-23 (PRMU, IBISML) |
| Conference Information | |
| Committee | PRMU / IBISML / IPSJ-CVIM |
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| Conference Date | 2023/3/2(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Future University Hakodate |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Seiichi Uchida(Kyushu Univ.) / Masashi Sugiyama(Univ. of Tokyo) |
| Vice Chair | Takuya Funatomi(NAIST) / Mitsuru Anpai(Denso IT Lab.) / Toshihiro Kamishima(AIST) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Takuya Funatomi(CyberAgent) / Mitsuru Anpai(Univ. of Tokyo) / Toshihiro Kamishima(NTT) / Koji Tsuda(Hokkaido Univ.) |
| Assistant | Nakamasa Inoue(Tokyo Inst. of Tech.) / Yasutomo Kawanishi(Riken) / Yoshinobu Kawahara(Osaka Univ.) / Taiji Suzuki(Tokyo Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding / Technical Committee on Information-Based Induction Sciences and Machine Learning / Special Interest Group on Computer Vision and Image Media |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data |
| Sub Title (in English) | |
| Keyword(1) | deep learningtendencyanomaly detectionsyntheticsimulation dataBlender |
| 1st Author's Name | Pei-Chun Chien |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Powei Liao |
| 2nd Author's Affiliation | Waseda University(Waseda Univ.) |
| 3rd Author's Name | Eiji Fukuzawa |
| 3rd Author's Affiliation | Waseda University(Waseda Univ.) |
| 4th Author's Name | Jun Ohya |
| 4th Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2023-03-03 |
| Paper # | PRMU2022-117,IBISML2022-124 |
| Volume (vol) | vol.122 |
| Number (no) | PRMU-404,IBISML-405 |
| Page | pp.pp.311-318(PRMU), pp.311-318(IBISML), |
| #Pages | 8 |
| Date of Issue | 2023-02-23 (PRMU, IBISML) |