Name
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 hand-crafted 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICTAI.2019.00085 | 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
Automatic Inspection, Convolution Neural Networks, Deep Learning, Visual Inspection, Texture | Visual inspection,Pattern recognition,Phase quantization,Computer science,Convolutional neural network,Artificial intelligence,Deep learning,Contextual image classification,Thermoelectric effect,Deep neural networks,Feature learning | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-7281-3799-5 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Vriesman | 1 | 0 | 0.34 |
| Alceu Souza Britto Junior | 2 | 0 | 0.34 |
| Alessandro Zimmer | 3 | 2 | 2.82 |
| Alessandro L. Koerich | 4 | 525 | 39.59 |