Damage Vision Mining Opportunity for Imbalanced Anomaly Detection

by   Takato Yasuno, et al.

In past decade, previous balanced datasets have been used to advance algorithms for classification, object detection, semantic segmentation, and anomaly detection in industrial applications. Specifically, for condition-based maintenance, automating visual inspection is crucial to ensure high quality. Deterioration prognostic attempts to optimize the fine decision process for predictive maintenance and proactive repair. In civil infrastructure and living environment, damage data mining cannot avoid the imbalanced data issue because of rare unseen events and high quality status by improved operations. For visual inspection, deteriorated class acquired from the surface of concrete and steel components are occasionally imbalanced. From numerous related surveys, we summarize that imbalanced data problems can be categorized into four types; 1) missing range of target and label valuables, 2) majority-minority class imbalance, 3) foreground-background of spatial imbalance, 4) long-tailed class of pixel-wise imbalance. Since 2015, there has been many imbalanced studies using deep learning approaches that includes regression, image classification, object detection, semantic segmentation. However, anomaly detection for imbalanced data is not yet well known. In the study, we highlight one-class anomaly detection application whether anomalous class or not, and demonstrate clear examples on imbalanced vision datasets: wooden, concrete deterioration, and disaster damage. We provide key results on damage vision mining advantage, hypothesizing that the more effective range of positive ratio, the higher accuracy gain of anomaly detection application. Finally, the applicability of the damage learning methods, limitations, and future works are mentioned.


page 6

page 7

page 9


Image anomaly detection with capsule networks and imbalanced datasets

Image anomaly detection consists in finding images with anomalous, unusu...

Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses

Urban and rural areas can often be devastated by extreme natural disaste...

ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

Nowadays, many industries have applied classification algorithms to help...

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

Computer vision-based damage detection using remote cameras and unmanned...

Per-pixel Classification Rebar Exposures in Bridge Eye-inspection

Efficient inspection and accurate diagnosis are required for civil infra...

Wooden Sleeper Decayed Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs

It is critical for railway managers to maintain a high standard to ensur...

SCNet: A Generalized Attention-based Model for Crack Fault Segmentation

Anomaly detection and localization is an important vision problem, havin...

Please sign up or login with your details

Forgot password? Click here to reset