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.
翻译:由于极端的服务环境和负荷因素,螺栓往往会松散甚至被解开。此外,对于实时或及时探测松散或松开的螺栓来说,实际工程是迫切需要的,这是保持结构安全和服务生命的关键。近年来,提出了许多使用深层学习和机器学习技术的螺栓解开的检测方法,并吸引了越来越多的关注。然而,这些研究大多使用实验室采集的螺栓图像进行深度精细精细模型培训。这些图像是以控制良好的光线、距离和角度条件获得的。此外,螺栓结构是设计良好的试验结构,有品牌新螺栓和螺栓,在实际工程中,对于保持结构安全和服务寿命至关重要。近年来,许多使用深层学习和机器学习技术的螺栓解开式检测方法已经提出,并且正在吸引越来越多的关注。但是,这些经过训练的螺栓图像在实验室条件中丢失了精确度或失败。因此,这一研究的目的是开发一个名为NPU-BOLLT的直径直径直线标的试验结构结构结构结构结构, 并且将一个名为NPU-BOL-BOLT的数据序列图象标的直径直径4,在自然标的自然标的模型中使用。