基于深度学习的变电设备缺陷检测与识别算法研究

Research on the Defect Detection and Identification Algorithm of Substation Equipment Based on Deep Learning

提出了一种基于深度学习的变电设备缺陷检测与识别算法。针对变电站环境下采集图像质量差的问题,首先采用生成对抗网络进行预处理以改善图像质量。随后采用改进的单阶段目标检测算法用于检测与识别变电设备的缺陷。结果表明,所提算法在缺陷识别精度和效率方面显著提升,为智能化变电站的监控与维护提供了有效的技术支持。

A defect detection and recognition algorithm for substation equipment based on deep learning is proposed.Aiming at the problem of poor image quality collected in substation environment,the generation countermeasure network is firstly used for preprocessing to improve the image quality.Then the improved single-stage target detection algorithm is used to detect and identify the defects of substation equipment.The results show that the proposed algorithm significantly improves the accuracy and efficiency of defect identification,and provides effective technical support for the monitoring and maintenance of intelligent substation.