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