Vision-based detection of loosened bolts using the Hough transform and support vector machines

Many contact-sensor-based methods for structural damage detection have been developed. However, these methods have difficulty compensating for environmental effects, such as variation or changes in temperature and humidity, which may lead to false alarms. In order to partially overcome these disadvantages, vision-based approaches have been developed to detect corrosions, cracks, delamination, and voids. However, there are few such approaches for loosened bolts. Therefore, we propose a novel vision-based detection method. Target images of loosened bolts were taken by a smartphone camera. From the images, simple damage-sensitive features, such as the horizontal and vertical lengths of the bolt head, were calculated automatically using the Hough transform and other image processing techniques. A linear support vector machine was trained with the aforementioned features, thereby building a robust classifier capable of automatically differentiating tight bolts from loose bolts. Leave-one-out cross-validation was adapted to analyze the performance of the proposed algorithm. The results highlight the excellent performance of the proposed approach to detecting loosened bolts, and that it can operate in quasi-real-time.