LAUSR

Vision-based structural displacement monitoring of rotor has attracted extensive attention from scholars due to its noncontact, nondamage, and multipoint synchronous measurement advantages. However, there is a lack of one comprehensive dataset and benchmark for rotor detection since the problem of limited acquisition equipment or on-site environment in the industrial field. For that, a large benchmark for rotor’s vibration displacement detection is introduced in this article. First, a rotor vibration displacement detection network (RDNet) for rotating structures is proposed, and state-of-the-art (SOTA) performance is achieved. Second, the evaluation criteria are designed to evaluate the rotor image detection accuracy using the mean average [email protected]:0.95 ([email protected]:0.95), the frames/s (FPS), and the normalized root mean squared error (NRMSE) as performance indicators. Third, a dataset, namely, Rotor V1.0, is built, which contains three frames (high, middle, and low) of 13500 rotor images. It is the rotor dataset for vibration displacement monitoring of rotating structures. Finally, the baseline performance resulted from traditional methods [i.e., match template (MT) and support vector machines (SVMs)] and deep-learning methods [i.e., CenterNet, faster region-convolutional neural networks (Faster R-CNNs), RetinaNet, single shot multibox detector (SSD), and you only look once (YOLO)] is reported and analyzed. It is hoped that the RDNet, evaluation criteria, and the novel Rotor V1.0 dataset can promote the implementation of visual vibration measurement tasks in industrial sites.