no code implementations • 24 Jul 2023 • Takato Yasuno
However, anomaly detection for imbalanced data is not well known.
no code implementations • 5 Jun 2023 • Takato Yasuno, Masahiro Okano, Junichiro Fujii
This study presents a new solution that offers a disaster anomaly detection application for initial responses with higher accuracy and devastation explainability, providing a novel contribution to the prompt disaster recovery problem in the research area of anomaly scene understanding.
no code implementations • 9 May 2023 • Takato Yasuno, Masahiro Okano, Junichiro Fujii
The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters.
no code implementations • 3 Mar 2023 • Takato Yasuno, Masahiro Okano, Junichiro Fujii
Furthermore, we propose a valuable solution of deeper FCDDs focusing on other powerful backbones to improve the performance of damage detection and implement ablation studies on disaster datasets.
no code implementations • 15 Jan 2023 • Takato Yasuno, Masahiro Okano, Junichiro Fujii
For infrastructure inspections, damage representation does not constantly match the predefined classes of damage grade, resulting in detailed clusters of unseen damages or more complex clusters from overlapped space between two grades.
no code implementations • 13 Jul 2022 • Takato Yasuno, Junichiro Fujii, Masazumi Amakata
We propose a patch-wise classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers.
no code implementations • 2 Mar 2022 • Takato Yasuno, Junichiro Fujii, Riku Ogata, Masahiro Okano
We prototype a method that combines auto-encoding reconstruction and isolation-based anomaly detector in application for road surface monitoring.
no code implementations • 6 Dec 2021 • Takato Yasuno, Masazumi Amakata, Junichiro Fujii, Masahiro Okano, Riku Ogata
It is important to forecast dam inflow for flood damage mitigation.
no code implementations • 30 Jun 2021 • Takato Yasuno, Junichiro Fujii, Sakura Fukami
Furthermore, we demonstrated our method through the inspection of steel sheet defects with 13, 774 unit images using high-speed cameras, and painted steel corrosion with 19, 766 unit images based on an eye inspection of the photographs.
no code implementations • 28 Feb 2021 • Takato Yasuno, Hiroaki Sugawara, Junichiro Fujii, Ryuto Yoshida
In 2021, Japan recorded more than three times as much snowfall as usual, so road user maybe come across dangerous situation.
no code implementations • 14 Jan 2021 • Takato Yasuno, Junichiro Fujii, Hiroaki Sugawara, Masazumi Amakata
Based on these trained networks, we automatically compute the road to snow rate hazard index, indicating the amount of snow covered on the road surface.
no code implementations • 30 Sep 2020 • Takato Yasuno, Akira Ishii, Masazumi Amakata
Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for flood damage mitigation.
no code implementations • 27 Jun 2020 • Takato Yasuno, Akira Ishii, Junichiro Fujii, Masazumi Amakata, Yuta Takahashi
When a damaged image is a generator input, it tends to reverse from the damaged state to the healthy state generated image.
no code implementations • 7 May 2020 • Takato Yasuno, Michihiro Nakajima, Tomoharu Sekiguchi, Kazuhiro Noda, Kiyoshi Aoyanagi, Sakura Kato
We propose a synthetic augmentation procedure to generate damaged images using the image-to-image translation mapping from the tri-categorical label that consists the both semantic label and structure edge to the real damage image.
no code implementations • 21 Apr 2020 • Takato Yasuno, Masazumi Amakata, Masahiro Okano
This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography.
no code implementations • 21 Apr 2020 • Takato Yasuno
Actually, we demonstrate several segmentation algorithms applied to the initial dataset that contains real images and labels using synthetic augmentation in order to add their generalized images.