Automated Vision-Based Loosened Bolt Detection Using the Cascade Detector

Numerous damage detection methods that use data obtained from contact sensors, physically attached to structures, have been developed. However, damage sensitive features used for these methods such as modal properties of steel and reinforced concrete structures are sensitive to environmental conditions such as temperature and humidity. These uncertainties are difficult to address with a regression model or any other temperature compensation method, and these are primary causes of false alarms. In order to address some of these challenges of the traditional sensing system, a vision-based remote sensing system can be one of the alternatives as it gives us explicit intuitions of structural conditions. In addition, bolted connections are common engineering practices, and very few vision-based techniques are developed for loosened bolt detection. Thus, this paper proposes an automated vision-based method for detecting loosened structural bolts using the Viola-Jones algorithm. Images of bolt connections are taken with a DSLR camera. The Viola-Jones algorithm is trained on two datasets of images with and without bolts. The trained algorithm localizes all bolts on images. The localized bolts are cropped and binarized to calculate bolt head dimensions and exposed shank length. The extracted features are fed into a support vector machine to generate a decision boundary separating loosened and tight bolts. We test our method on images taken by DSLR and smartphone cameras.