  Bolt joints are very common and important in engineering structures. Due to
extreme service environment and load factors, bolts often get loose or even
disengaged. To real-time or timely detect the loosed or disengaged bolts is an
urgent need in practical engineering, which is critical to keep structural
safety and service life. In recent years, many bolt loosening detection methods
using deep learning and machine learning techniques have been proposed and are
attracting more and more attention. However, most of these studies use bolt
images captured in laboratory for deep leaning model training. The images are
obtained in a well-controlled light, distance, and view angle conditions. Also,
the bolted structures are well designed experimental structures with brand new
bolts and the bolts are exposed without any shelter nearby. It is noted that in
practical engineering, the above well controlled lab conditions are not easy
realized and the real bolt images often have blur edges, oblique perspective,
partial occlusion and indistinguishable colors etc., which make the trained
models obtained in laboratory conditions loss their accuracy or fails.
Therefore, the aim of this study is to develop a dataset named NPU-BOLT for
bolt object detection in natural scene images and open it to researchers for
public use and further development. In the first version of the dataset, it
contains 337 samples of bolt joints images mainly in the natural environment,
with image data sizes ranging from 400*400 to 6000*4000, totaling approximately
1275 bolt targets. The bolt targets are annotated into four categories named
blur bolt, bolt head, bolt nut and bolt side. The dataset is tested with
advanced object detection models including yolov5, Faster-RCNN and CenterNet.
The effectiveness of the dataset is validated.
