Wang Weihua, Chen Jiugong, Han Gangsheng, Shi Xiushan, Qian Gong
State Key Laboratory of Low-Carbon Thermal Power Generation Technology and Equipments, China Special Equipment Inspection and Research Institute, Beijing 100029, China.
China Special Equipment Inspection and Research Institute, Beijing 100029, China.
Sensors (Basel). 2024 Sep 13;24(18):5944. doi: 10.3390/s24185944.
Non-destructive testing (NDT) techniques play a crucial role in industrial production, aerospace, healthcare, and the inspection of special equipment, serving as an indispensable part of assessing the safety condition of pressure equipment. Among these, the analysis of NDT data stands as a critical link in evaluating equipment safety. In recent years, object detection techniques have gradually been applied to the analysis of NDT data in pressure equipment inspection, yielding significant results. This paper comprehensively reviews the current applications and development trends of object detection algorithms in NDT technology for pressure-bearing equipment, focusing on algorithm selection, data augmentation, and intelligent defect recognition based on object detection algorithms. Additionally, it explores open research challenges of integrating GAN-based data augmentation and unsupervised learning to further enhance the intelligent application and performance of object detection technology in NDT for pressure-bearing equipment while discussing techniques and methods to improve the interpretability of deep learning models. Finally, by summarizing current research and offering insights for future directions, this paper aims to provide researchers and engineers with a comprehensive perspective to advance the application and development of object detection technology in NDT for pressure-bearing equipment.
无损检测（NDT）技术在工业生产、航空航天、医疗保健以及特殊设备检测中发挥着至关重要的作用，是评估压力设备安全状况不可或缺的一部分。其中，无损检测数据分析是评估设备安全性的关键环节。近年来，目标检测技术逐渐应用于压力设备检测中的无损检测数据分析，并取得了显著成果。本文全面综述了目标检测算法在承压设备无损检测技术中的当前应用和发展趋势，重点关注算法选择、数据增强以及基于目标检测算法的智能缺陷识别。此外，探讨了基于生成对抗网络（GAN）的数据增强与无监督学习相结合的开放研究挑战，以进一步提升目标检测技术在承压设备无损检测中的智能应用和性能，同时讨论提高深度学习模型可解释性的技术和方法。最后，通过总结当前研究并为未来方向提供见解，本文旨在为研究人员和工程师提供一个全面的视角，以推动目标检测技术在承压设备无损检测中的应用和发展。
