Abstract
In this paper, the concept of representation learning based on deep neural networks is applied as an alternative to the use of handcrafted features in a method for automatic visual inspection of corroded thermoelectric metallic pipes. A texture convolutional neural network (TCNN) replaces handcrafted features based on Local Phase Quantization (LPQ) and Haralick descriptors (HD) with the advantage of learning an appropriate textural representation and the decision boundaries into a single optimization process. Experimental results have shown that it is possible to reach the accuracy of 99.20% in the task of identifying different levels of corrosion in the internal surface of thermoelectric pipe walls, while using a compact network that requires much less effort in tuning parameters when compared to the handcrafted approach since the TCNN architecture is compact regarding the number of layers and connections. The observed results open up the possibility of using deep neural networks in real-time applications such as the automatic inspection of thermoelectric metal pipes.
Abstract (translated)
本文将基于深度神经网络的表示学习概念应用于热电腐蚀金属管道的自动视觉检测中，以替代手工特征的使用。纹理卷积神经网络（TCNN）取代了基于局部相位量化（LPQ）和哈拉利克描述子（HD）的手工制作的特征，其优点是学习一个合适的纹理表示和决策边界，形成一个单一的优化过程。实验结果表明，在识别热电管壁内表面不同程度腐蚀的任务中，可以达到99.20%的准确度，同时使用一个紧凑的网络，与手工方法相比，该网络在参数调整方面所需的工作量要小得多，因为TCNN架构关于层数和连接的契约。实验结果为深神经网络在热电金属管道自动检测等实时应用中的应用提供了可能。
URL
https://arxiv.org/abs/1905.12003
