The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

输水隧道的检查在引水工程中起着重要的作用。淤积是威胁输水隧道安全的重要因素。准确有效地识别此类淤积可以降低风险并提高这些项目的安全性和可靠性。遥控潜水器 (ROV) 可以检测到这种淤积。但是，它需要改进其对获得的图像数据的智能识别。本文介绍了集成深度学习的思想。基于VGG16网络，设计了一个紧凑的卷积神经网络（CNN）作为主要学习器，称为Silt-net，用于识别淤积图像。同时将全连接网络作为元学习器，堆叠集成学习与初级分类器的输出相结合，得到满意的分类结果。最后，使用几个评估指标来衡量所提出方法的性能。在淤积数据集上的实验结果表明，该方法的分类准确率达到了97.2%，远优于其他分类器的准确率。此外，所提出的方法可以在计算资源有限的平台上权衡准确性和模型复杂性。