基于迁移学习卷积神经网络的电缆隧道锈蚀识别算法

随着电力行业的不断发展，高压电缆的铺排以及地下电缆隧道的建设与维护逐渐成为该领域中的热点问题之一。将迁移学习的核心思想与经典的卷积神经网络（LeNet5）相结合，提出了一种基于迁移学习卷积神经网络的电缆隧道锈蚀识别算法，实现了电缆隧道内部电源箱、风机等设备的锈蚀识别。该算法基于Tensorflow框架，能够有效地解决训练样本不足、训练时间冗长以及识别精度不高的问题。通过引入4种经典的目标识别算法进行对比实验，进一步验证了所提方案在网络训练时间以及识别精确度上的优势，为后续电缆隧道巡检机器人系统的构建提供了坚实的理论基础与实验支撑。

A Hybrid Transfer Learning/CNN Algorithm for Cable Tunnel Rust Recognition

With the continuous development of the power industry, the laying of high-voltage cables and the construction and maintenance of underground cable tunnels have gradually become one of the hot issues in this field. This paper proposes a cable tunnel rust recognition method based on the integration of transfer learning and the classical convolutional neural network (LeNet5), which is able to realize the specific rust recognition of some electrical devices, such as the internal power box, fan and other equipment. The whole recognition process is based on the Tensorflow framework, and is able to solve such problems as insufficient training samples, long training time, and low recognition accuracy. Furthermore, by comparing with four classical target recognition algorithms, the proposed scheme is proved to be superior in terms of training time and recognition accuracy. The entire scheme provides a solid theoretical basis and experimental support for the realization of subsequent cable tunnel inspection robot system.