Semi-supervised Bolt Anomaly Detection in Haphazard Environment
Chuang-Wei Liu, Yi Yan, Nachuan Ma, Yun Peng, Chengju Liu, Qijun Chen · 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) · 2022
Grease injection is one of the most important parts of high-speed rail maintenance, and whether the bolts on related facilities contain anomaly plays a crucial role in the safety of the entire grease injection process. Bolt anomaly detection is typically performed by certified inspectors. However, this task is not only hazardous for the personnel but also time-consuming. The decisions that depend entirely on the individuals’ experiences are subjective. Therefore, this article presents an efficient bolt anomaly detection framework based on a semi-supervised technique. We first train a novel U-Net-based neural network with anomaly-free samples, which can simultaneously predict the positions of all bolts and segment areas that contain anomaly-free bolts. Subsequently, we build a novel variance distribution model to extract the prior knowledge of areas that contain anomaly-free bolts based on the segmentation results and predicted position results from the network. Finally, we utilize the pre-built model to generate anomaly response maps of test images, which intuitively visualize the areas containing abnormal bolts. The experimental results demonstrate that our proposed bolt anomaly detection framework achieves satisfactory accuracy and efficiency, which can meet the requirements of practical engineering detection.