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Found 16 papers, 0 papers with code
Few-shot $\mathbf{1/a}$ Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance
However, anomaly detection for imbalanced data is not well known.
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses
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.
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs
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.
One-class Damage Detector Using Deeper Fully-Convolutional Data Descriptions for Civil Application
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.
MN-Pair Contrastive Damage Representation and Clustering for Prognostic Explanation
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.
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index
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.
VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface
We prototype a method that combines auto-encoding reconstruction and isolation-based anomaly detector in application for road surface monitoring.
Flood Inflow Forecast Using L2-norm Ensemble Weighting Sea Surface Feature
It is important to forecast dam inflow for flood damage mitigation.
One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map
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.
Snowy Night-to-Day Translator and Semantic Segmentation Label Similarity for Snow Hazard Indicator
In 2021, Japan recorded more than three times as much snowfall as usual, so road user maybe come across dangerous situation.
Road Surface Translation Under Snow-covered and Semantic Segmentation for Snow Hazard Index
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.
Rain-Code Fusion : Code-to-code ConvLSTM Forecasting Spatiotemporal Precipitation
Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for flood damage mitigation.
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images
When a damaged image is a generator input, it tends to reverse from the damaged state to the healthy state generated image.
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge
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.
Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentation
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.
Natural Disaster Classification using Aerial Photography Explainable for Typhoon Damaged Feature
This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography.
