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Structural bolts are critical components used in different structuralelements, such as beam-column connections and friction damping devices. Theclamping force in structural bolts is highly influenced by the bolt rotation.Much of the existing vision-based research about bolt rotation estimationrelies on traditional computer vision algorithms such as Hough Transform toassess static images of bolts. This requires careful image preprocessing, andit may not perform well in the situation of complicated bolt assemblies, or inthe presence of surrounding objects and background noise, thus hindering theirreal-world applications. In this study, an integrated real-time detect-trackmethod, namely RTDT-Bolt, is proposed to monitor the bolt rotation angle.First, a real-time convolutional-neural-networks-based object detector, namedYOLOv3-tiny, is established and trained to localize structural bolts. Then, thetarget-free object tracking algorithm based on optical flow is implemented, tocontinuously monitor and quantify the rotation of structural bolts. In order toenhance the tracking performance against background noise and potentialillumination changes during tracking, the YOLOv3-tiny is integrated with theoptical flow tracking algorithm to re-detect the bolts when the tracking getslost. Extensive parameter studies were conducted to identify optimal trackingperformance and examine the potential limitations. The results indicate theRTDT-Bolt method can greatly enhance the tracking performance of bolt rotation,which can achieve over 90% accuracy using the recommended range for theparameters.

Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking