Detection of corrosion on steel structures using an artificial neural network

Image based-corrosion detection has become a widespread practice for steel structures, but fine-tuning their model parameters is time-consuming. Alternatively, convolutional neural networks (CNNs) can also be trained fast and automatically, but they demand a huge training dataset. In this paper, a corrosion detection approach based on an artificial neural network (ANN) whose training dataset size is less than 0.1% of that of typical CNNs is introduced. The input layer of the proposed ANN consists of textural and color properties. In the present work, different color spaces and textural properties are examined for their impact on the robustness of the ANN. Results reveal that the best color channels can be achieved by combining <i>CIE L*u*v*</i> and <i>YUV</i> color spaces. Moreover, <i>energy</i> is selected as the best texture feature with respect to the ANN robustness. The proposed ANN outperforms an available image processing algorithm from the perspective of both speed and accuracy. In conclusion, this ANN can be used for actual applications after a fast and straightforward training step.基于图像的腐蚀检测已成为钢结构领域的通用检测手段，但对其模型参数进行微调往往耗时良久。另一种方案是，卷积神经网络（Convolutional Neural Networks, CNNs）虽可实现快速自动训练，但这类模型需要海量训练数据集。本文提出了一种基于人工神经网络（Artificial Neural Network, ANN）的腐蚀检测方法，其训练数据集规模仅为典型卷积神经网络训练数据集的0.1%以下。所提人工神经网络的输入层由纹理与色彩特征构成。本研究针对不同色彩空间与纹理特征对人工神经网络鲁棒性的影响展开了系统探究。研究结果显示，将CIE L*u*v*与YUV色彩空间相结合，可获得最优的色彩通道组合。此外，就人工神经网络的鲁棒性而言，能量（energy）特征被选为最优纹理特征。所提人工神经网络在运行速度与检测精度两方面均优于现有图像处理算法。综上，该人工神经网络仅需经过快速且简便的训练步骤，即可投入实际应用。