Exposed aggregate is a typical feature of the abrasion erosion in stilling basin slabs concrete surface. Although a variety of underwater robots are designed for inspection, the exposed aggregate detection for identifying abrasion is often done by manual work. The scarcity of image samples, large differences in aggregate size, color and shape are the main difficulties in automatic detection. To address this problem, an improved Attention U-Net deep fully convolutional network-based detection method was proposed. To realize this method, underwater images in site were captured via a self-developed operated underwater robot. Through randomly separating and the cropping of the 128 underwater images, the 512×512 pixels images dataset was built according to the ratio of 8:1:1, including 408 training images, 52 validation images and 52 test images. After the data augmentation, loss function and the optimizer were carefully designed and selected, the proposed Attention U-Net architecture was evaluated on this dataset. For comparative research, the full convolution network (FCN) and U-Net network were trained with the same training and validation dataset. The performance comparison on the test dataset showed that the Attention U-Net architecture has better detection accuracy.

裸露骨料是静水盆地板混凝土表面磨蚀的典型特征。尽管设计了多种水下机器人进行检查，但是用于识别磨损的裸露集料检测通常是通过人工完成的。图像样本的稀缺，聚集体大小，颜色和形状的巨大差异是自动​​检测的主要困难。为了解决这个问题，提出了一种改进的基于注意力U-Net深度全卷积网络的检测方法。为了实现这种方法，现场的水下图像是通过自行开发的水下机器人捕获的。通过随机分割和裁剪128幅水下图像，按照8：1：1的比例建立了512×512像素的图像数据集，包括408张训练图像，52张验证图像和52张测试图像。在精心设计和选择了数据扩充，损失函数和优化器之后，对该数据集评估了拟议的Attention U-Net体系结构。为了进行比较研究，使用相同的训练和验证数据集对完整卷积网络（FCN）和U-Net网络进行了训练。在测试数据集上的性能比较表明，Attention U-Net体系结构具有更好的检测准确性。