Computer vision based decision support tool for hydro-dams surface deterioration assessment and visualization using fuzzy sets and pseudo-coloring

Hydro-dams safety represents an important concern since their failure could be critical for the society. A key part of the hydro-dams surveillance programs is their visual inspection. However few computer vision support tools for implementing semi-automatically and objectively the visual surveillance and observation of the hydro-dams components exist. One of the issues addressed during the visual inspection, important in the preservation of a good condition of the concrete, is the examination of surface deterioration in respect to small patterned cracks and roughness on the downstream wall. This is particularly a task where digital image enhancement and analysis can bring significant benefit, not only by presenting the user with a more relevant image of the surface deterioration, but also by providing - through suitable numerical descriptors, correlated with linguistic descriptors- subjective and examiner-independent information on the surface state. The correlation of extracted numerical descriptors used to quantify the surface roughness with linguistic qualifiers of the deterioration state of the hydro-dam wall should be determined using information gathered from observers, since it must be compliant to the human expert interpretation of visual data in assessing the concrete surface deterioration. Such an approach would result in a computer vision decision support tool embedding expert knowledge, as designed, implemented and proposed in this paper. The resulting software system was verified on a set of images acquired from a Romanian hydro-dam. The compliance of the linguistic results with the human observation proves its functionality as a semi-automatic tool for hydro-dams surveillance.