STEEL DEFECT DETECTION IN BRIDGES USING DEEP ENCODER-DECODER NETWORKS
STEEL DEFECT DETECTION IN BRIDGES USING DEEP ENCODER-DECODER NETWORKS
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DOI：
10.12783/shm2021/36335
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Habib Ahmed;H. La
中科院分区：
文献类型：
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作者：
Habib Ahmed;H. La
最近与桥梁有关的重大事故强调了开发有效的缺陷检测技术解决方案的必要性，这可以最大限度地减少未来与桥梁有关的事故的可能性。在这方面，本研究将侧重于开发用于检测桥梁不同钢构件内部缺陷区域的自动化系统。目前，还没有开源的图像数据集可以用于此目的。因此，训练数据集是通过使用从越南桥梁获取的图像来开发的，并使用位于美国内华达州拉夫洛克80号高速公路上的拉夫洛克大桥获取的图像进行验证。总共使用了5500张不同维度的图像（4000张用于训练，1500张用于验证），钢桥图像的原始尺寸经过572 × 572像素的修改，用于不同深度编码器-解码器网络上的数据集训练和评估。使用来自不同桥接的不同数据将允许开发一个强大的深度编码器-解码器网络，对未来的实际系统具有相当大的影响。本研究将采用最先进的深度编码器-解码器网络，该网络最近已开发用于其他应用。然而，目前还没有针对钢桥缺陷检测的相关研究。将对不同深度编码器-解码器网络进行比较评估。同时，将该系统的性能与最近的先进方法进行比较。结果表明，深度编码器-解码器在钢桥缺陷检测方面具有相当大的潜力，将在未来的研究中进一步开发。
Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies.