Ensembled convolutional neural network (ECNN) was utilized to recognize the rust grade and rust ratio of steel structure to partially replace traditional visual inspection. The performance of ECNN was demonstrated by theoretical analysis and experimental verification, and the application scenarios of ECNN in the task of rust grade recognition and rust ratio recognition were discussed. The accuracy of ECNN classifier reached 93%, which improves upon the highest accuracy of 90% achieved by using a single classifier. By visualizing the misclassified images, it was found that the rust grade of misclassified image is indistinguishable and the classifiers show strong fault tolerance. The ensembled model is more robust than the single model in the task of rust ratio recognition. Gaussian blur was applied to the test images to study the effect of image blur on model performance, and the results show that the rust segmentation model was not susceptible to image blur.

中文翻译：

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基于卷积神经网络的钢结构锈等级和锈比识别

利用集成卷积神经网络（ECNN）识别钢结构的锈等级和锈比，以部分替代传统的目视检查。通过理论分析和实验验证，证明了ECNN的性能，并讨论了ECNN在锈等级识别和锈比识别任务中的应用场景。ECNN分类器的准确性达到93％，与使用单个分类器实现的90％的最高准确性相比有所改善。通过对错误分类的图像进行可视化，发现错误分类的图像的锈等级难以区分，并且分类器显示出强大的容错能力。集成模型在锈蚀率识别任务中比单一模型更健壮。