  Over the past decade, previous balanced datasets have been used to advance
deep learning algorithms for industrial applications. In urban infrastructures
and living environments, damage data mining cannot avoid imbalanced data issues
because of rare unseen events and the high-quality status of improved
operations. For visual inspection, the deteriorated class acquired from the
surface of concrete and steel components are occasionally imbalanced. From
numerous related surveys, we conclude that imbalanced data problems can be
categorised into four types: 1) missing range of target and label valuables, 2)
majority-minority class imbalance, 3) foreground background of spatial
imbalance, and 4) long-tailed class of pixel-wise imbalance. Since 2015, many
imbalanced studies have been conducted using deep-learning approaches,
including regression, image classification, object detection, and semantic
segmentation. However, anomaly detection for imbalanced data is not well known.
In this study, we highlight a one-class anomaly detection application, whether
anomalous class or not, and demonstrate clear examples of imbalanced vision
datasets: medical disease, hazardous behaviour, material deterioration, plant
disease, river sludge, and disaster damage. We provide key results on the
advantage of damage-vision mining, hypothesising that the more effective the
range of the positive ratio, the higher the accuracy gain of the anomalies
feedback. In our imbalanced studies, compared with the balanced case with a
positive ratio of $1/1$, we find that there is an applicable positive ratio
$1/a$ where the accuracy is consistently high. However, the extremely
imbalanced range is from one shot to $1/2a$, the accuracy of which is inferior
to that of the applicable ratio. In contrast, with a positive ratio ranging
over $2/a$, it shifts in the over-mining phase without an effective gain in
accuracy.
