论文标题
NPU螺栓：自然场景图像中用于螺栓对象检测的数据集
NPU-BOLT: A Dataset for Bolt Object Detection in Natural Scene Images
论文作者
论文摘要
螺栓接头在工程结构中非常普遍且重要。由于极端的服务环境和负载因素，螺栓通常会松动甚至脱离。实时或及时检测叶片或脱离螺栓是实际工程的迫切需要，这对于保持结构安全和使用寿命至关重要。近年来，已经提出了使用深度学习和机器学习技术的许多螺栓松动检测方法，并吸引了越来越多的关注。但是，这些研究中的大多数使用在实验室中捕获的螺栓图像进行深度倾斜模型训练。图像是在控制良好的光，距离和视角条件下获得的。此外，螺栓结构是具有全新螺栓的精心设计的实验结构，并且螺栓在附近没有任何庇护所的情况下暴露。值得注意的是，在实践工程中，上述良好的实验室条件并不容易实现，真正的螺栓图像通常具有模糊的边缘，倾斜的透视图，部分遮挡和无法区分的颜色等，这使得在实验室条件下获得的经过训练的模型损失了其准确性或失败。因此，这项研究的目的是开发一个名为NPU螺栓的数据集，用于自然场景图像中的螺栓对象检测，并向研究人员开放公众使用和进一步开发。在数据集的第一个版本中，它包含337个螺栓接头图像的样本，主要在自然环境中，图像数据尺寸范围为400*400至6000*4000，总计约1275个螺栓目标。螺栓靶标被注释分为四类，称为Blur Bolt，Bolt头，螺栓螺母和螺栓侧。使用高级对象检测模型（包括Yolov5，更快的RCNN和Centernet）对数据集进行了测试。数据集的有效性已验证。
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.
