# Texture CNN for Thermoelectric Metal Pipe Image Classification

## TL;DR

This paper introduces a texture CNN that automates corrosion detection in thermoelectric pipes, achieving high accuracy with a compact model suitable for real-time inspection.

## Contribution

It presents a deep neural network approach replacing handcrafted features for corrosion classification, simplifying tuning and improving accuracy.

## Key findings

- Achieved 99.20% accuracy in corrosion level identification
- The proposed TCNN is compact and requires less parameter tuning
- Demonstrated potential for real-time pipe inspection applications

## 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.