Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection
Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection
复制标题
DOI：
10.1177/1475921717737051
复制
发表时间：
2018-09-01
影响因子：
6.6
通讯作者：
Jahanshahi, Mohammad R.
中科院分区：
文献类型：
--
作者：
Atha, Deegan J.;Jahanshahi, Mohammad R.
腐蚀是结构系统中的一个主要缺陷，具有重大的经济影响，如果不加以处理，可能会造成安全风险。目前，检查员通过视觉评估结构的状况来识别腐蚀。这种方法是耗时的、乏味的和主观的。机器人系统，如无人驾驶飞行器，与计算机视觉算法相结合，有可能执行自主的损坏检测，可以显着减少检查时间，并导致更频繁和客观的检查。本研究评估了卷积神经网络在腐蚀检测中的应用。卷积神经网络学习适当的分类特征，这些特征在传统算法中是手工设计的。在设计特征时消除对先验知识和人工努力的依赖是卷积神经网络的一个主要优势。本文介绍了不同的基于卷积神经网络的金属表面腐蚀评估方法。讨论了不同颜色空间、滑动窗口大小和卷积神经网络架构的影响。为此，评估了两种预训练的最先进的卷积神经网络架构以及两种拟议的卷积神经网络架构的性能，结果表明，卷积神经网络优于基于纹理和颜色分析开发的基于视觉的腐蚀检测方法，这些方法使用简单的多层感知器网络。此外，与最先进的预训练卷积神经网络相比，所提出的卷积神经网络之一显着提高了计算时间，同时保持了相当的腐蚀检测性能。
Corrosion is a major defect in structural systems that has a significant economic impact and can pose safety risks if left untended. Currently, an inspector visually assesses the condition of a structure to identify corrosion. This approach is time-consuming, tedious, and subjective. Robotic systems, such as unmanned aerial vehicles, paired with computer vision algorithms have the potential to perform autonomous damage detection that can significantly decrease inspection time and lead to more frequent and objective inspections. This study evaluates the use of convolutional neural networks for corrosion detection. A convolutional neural network learns the appropriate classification features that in traditional algorithms were hand-engineered. Eliminating the need for dependence on prior knowledge and human effort in designing features is a major advantage of convolutional neural networks. This article presents different convolutional neural network-based approaches for corrosion assessment on metallic surfaces. The effect of different color spaces, sliding window sizes, and convolutional neural network architectures are discussed. To this end, the performance of two pretrained state-of-the-art convolutional neural network architectures as well as two proposed convolutional neural network architectures are evaluated, and it is shown that convolutional neural networks outperform state-of-the-art vision-based corrosion detection approaches that are developed based on texture and color analysis using a simple multilayered perceptron network. Furthermore, it is shown that one of the proposed convolutional neural networks significantly improves the computational time in contrast with state-of-the-art pretrained convolutional neural networks while maintaining comparable performance for corrosion detection.