| Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine P Chun, S Izumi, T Yamane Computer‐Aided Civil and Infrastructure Engineering 36 (1), 61-72, 2021 | 231 | 2021 |
| A deep learning‐based image captioning method to automatically generate comprehensive explanations of bridge damage PJ Chun, T Yamane, Y Maemura Computer‐Aided Civil and Infrastructure Engineering 37 (11), 1387-1401, 2022 | 118 | 2022 |
| Crack Detection from a Concrete Surface Image Based on Semantic Segmentation Using Deep Learning T Yamane, P Chun Journal of Advanced Concrete Technology 18 (9), 493-504, 2020 | 107 | 2020 |
| Automatic Detection of Cracks in Asphalt Pavement Using Deep Learning to Overcome Weaknesses in Images and GIS Visualization P Chun, T Yamane, Y Tsuzuki Applied Sciences 11 (3), 892, 2021 | 61 | 2021 |
| Evaluation of Tensile Performance of Steel Members by Analysis of Corroded Steel Surface Using Deep Learning P Chun, T Yamane, S Izumi, T Kameda Metals 9 (12), 1259, 2019 | 53 | 2019 |
| Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results P Chun, T Yamane, S Izumi, N Kuramoto Sensors 20 (10), 2780, 2020 | 45 | 2020 |
| Recording of bridge damage areas by 3D integration of multiple images and reduction of the variability in detected results T Yamane, P Chun, J Dang, R Honda Computer‐Aided Civil and Infrastructure Engineering 38 (17), 2391-2407, 2023 | 43 | 2023 |
| Utilization of Unmanned Aerial Vehicle, Artificial Intelligence, and Remote Measurement Technology for Bridge Inspections P Chun, J Dang, S Hamasaki, R Yajima, T Kameda, H Wada, T Yamane, ... Journal of Robotics and Mechatronics 32 (6), 1244-1258, 2020 | 41 | 2020 |
| Detecting and localising damage based on image recognition and structure from motion, and reflecting it in a 3D bridge model T Yamane, P Chun, R Honda Structure and Infrastructure Engineering 20 (4), 594-606, 2024 | 36 | 2024 |
| Deep learning による Semantic Segmentation を用いたコンクリート表面ひび割れの検出 山根達郎， 全邦釘 構造工学論文集 A 65, 130-138, 2019 | 34 | 2019 |
| Improving visual question answering for bridge inspection by pre‐training with external data of image–text pairs T Kunlamai, T Yamane, M Suganuma, PJ Chun, T Okatani Computer‐Aided Civil and Infrastructure Engineering 39 (3), 345-361, 2024 | 22 | 2024 |
| Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method S Kubo, T Yamane, P Chun Sensors 22 (17), 6412, 2022 | 19 | 2022 |
| Deep learning-based bridge damage cause estimation from multiple images using visual question answering T Yamane, P Chun, J Dang, T Okatani Structure and Infrastructure Engineering, 1-14, 2024 | 15* | 2024 |
| Deep Learning による橋梁撮影画像からの損傷状況説明文の自動生成 山根達郎， 全邦釘， 渡部達也 土木学会論文集 F3 (土木情報学) 77 (2), I_40-I_50, 2021 | 13 | 2021 |
| Implementation of explanatory texts output for bridge damage in a bridge inspection web system P Chun, H Chu, K Shitara, T Yamane, Y Maemura Advances in Engineering Software 195, 103706, 2024 | 9 | 2024 |
| 苦手タイプ改善型ディープラーニングを用いたアスファルト舗装のひび割れ自動検出 都築幸乃， 全邦釘， 山根達郎 AI・データサイエンス論文集 1 (J1), 168-179, 2020 | 7 | 2020 |
| Attention 機構モデルによる橋梁撮影画像からの損傷状況推定 山根達郎， 渡部達也， 全邦釘 AI・データサイエンス論文集 2 (J2), 632-641, 2021 | 5* | 2021 |
| Semantic Segmentation を用いた橋梁 3 次元モデルへのひび割れ位置の反映 山根達郎， 上野雄也， 叶井和樹， 泉翔太， 全邦釘 AI・データサイエンス論文集 1 (J1), 491-497, 2020 | 5 | 2020 |
| インフラメンテナンスにおける専門知の構造 杉崎光一， 阿部雅人， 山根達郎， 全邦釘 AI・データサイエンス論文集 2 (J2), 8-19, 2021 | 4 | 2021 |
| Semantic Segmentation を用いた斜面崩壊領域の自動検出 叶井和樹， 山根達郎， 石黒聡士， 全邦釘 AI・データサイエンス論文集 1 (J1), 421-428, 2020 | 4 | 2020 |
Pang-jo ChunThe University of Tokyo確認したメール アドレス: i-con.t.u-tokyo.ac.jp
