| Presentation | 2020-03-06 Detection of running area from forest road images with different image quality using deep learning Misato Ushiro, Tetsuya Higashino, Yuukou Horita, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Natural disasters such as falling rocks and landslides are increasing year by year on forest roads in hilly and mountainous areas. It is expected that accidents can be mitigated if it is possible to accurately detect and share information for the danger place caused by falling rocks and landslides on forest roads obtained by using images acquired from car mounted cameras. However, there is no image data set with a label for road images where landslides and road cracks that interfere with traffic due to natural disasters. In this study, we investigate a method for accurately detecting driving possible area on forest roads by actually taking forest roads and deep learning using collected image data sets. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Image processing / Forest roads / Semantic image segmentation |
| Paper # | IMQ2019-60,IE2019-142,MVE2019-81 |
| Date of Issue | 2020-02-27 (IMQ, IE, MVE) |
| Conference Information | |
| Committee | IE / IMQ / MVE / CQ |
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| Conference Date | 2020/3/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyushu Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hideaki Kimata(NTT) / Toshiya Nakaguchi(Chiba Univ.) / Kenji Mase(Nagoya Univ.) / Hideyuki Shimonishi(NEC) |
| Vice Chair | Kazuya Kodama(NII) / Keita Takahashi(Nagoya Univ.) / Mitsuru Maeda(Canon) / Kenya Uomori(Osaka Univ.) / Masayuki Ihara(NTT) / Jun Okamoto(NTT) / Takefumi Hiraguri(Nippon Inst. of Tech.) |
| Secretary | Kazuya Kodama(NTT) / Keita Takahashi(NHK) / Mitsuru Maeda(Shizuoka Univ.) / Kenya Uomori(Sony Semiconductor Solutions) / Masayuki Ihara(Nagoya Univ.) / Jun Okamoto(NTT) / Takefumi Hiraguri(Nippon Inst. of Tech.) |
| Assistant | Kyohei Unno(KDDI Research) / Norishige Fukushima(Nagoya Inst. of Tech.) / Hiroaki Kudo(Nagoya Univ.) / Masaru Tsuchida(NTT) / Keita Hirai(Chiba Univ.) / Satoshi Nishiguchi(Oosaka Inst. of Tech.) / Masanori Yokoyama(NTT) / Shogo Fukushima(Univ. of ToKyo) / Chikara Sasaki(KDDI Research) / Yoshiaki Nishikawa(NEC) / Takuto Kimura(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Image Engineering / Technical Committee on Image Media Quality / Technical Committee on Media Experience and Virtual Environment / Technical Committee on Communication Quality |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Detection of running area from forest road images with different image quality using deep learning |
| Sub Title (in English) | |
| Keyword(1) | Image processing |
| Keyword(2) | Forest roads |
| Keyword(3) | Semantic image segmentation |
| 1st Author's Name | Misato Ushiro |
| 1st Author's Affiliation | University of Toyama(Univ. of Toyama) |
| 2nd Author's Name | Tetsuya Higashino |
| 2nd Author's Affiliation | University of Toyama(Univ. of Toyama) |
| 3rd Author's Name | Yuukou Horita |
| 3rd Author's Affiliation | University of Toyama(Univ. of Toyama) |
| Date | 2020-03-06 |
| Paper # | IMQ2019-60,IE2019-142,MVE2019-81 |
| Volume (vol) | vol.119 |
| Number (no) | IMQ-454,IE-456,MVE-457 |
| Page | pp.pp.231-234(IMQ), pp.231-234(IE), pp.231-234(MVE), |
| #Pages | 4 |
| Date of Issue | 2020-02-27 (IMQ, IE, MVE) |