Bolt loosening angle detection technology using deep learning

Abstract

As an important part of the steel structure, the bolt damage will affect the safety of the structure and even cause severe accidents. However, it is difficult to detect the bolt loosening from the perspective of the conventional dynamics, due to the complex vibration characteristics of the bolt joints. In order to detect structural damage intuitively, machine vision has been introduced into the field of structural health monitoring. Therefore, this paper combines deep learning and machine vision to propose a bolt loosening angle detection technology. First, the data sets with bolts were collected and divided into training sets, validation sets, and test sets. Second, the data sets were trained using Single Shot MultiBox Detector. And the recognition accuracy of the model was evaluated, which can reach 0.914. Thereafter, the images obtained from different angles and lighting conditions were detected by the training model; the results showed that this method still has high recognition accuracy and meets the requirements of engineering. Finally, the training model was migrated to the smartphone to achieve quick and simple bolt loosening monitoring.