Deep learning techniques have attracted significant attention in the field of visual inspection of civil infrastructure systems recently. Currently, most deep learning-based visual inspection techniques utilize a convolutional neural network to recognize surface defects either by detecting a bounding box of each defect or classifying all pixels on an image without distinguishing between different defect instances. These outputs cannot be directly used for acquiring the geometric properties of each individual defect in an image, thus hindering the development of fully automated structural assessment techniques. In this study, a novel fully convolutional model is proposed for simultaneously detecting and grouping the image pixels for each individual defect on an image. The proposed model integrates an optimized mask subnet with a box-level detection network, where the former outputs a set of position-sensitive score maps for pixel-level defect detection and the latter predicts a bounding box for each defect to group the detected pixels. An image dataset containing three common types of concrete defects, crack, spalling and exposed rebar, is used for training and testing of the model. Results demonstrate that the proposed model is robust to various defect sizes and shapes and can achieve a mask-level mean average precision (mAP) of 82.4% and a mean intersection over union (mIoU) of 75.5%, with a processing speed of about 10 FPS at input image size of 576 × 576 when tested on an NVIDIA GeForce GTX 1060 GPU. Its performance is compared with the state-of-the-art instance segmentation network Mask R-CNN and the semantic segmentation network U-Net. The comparative studies show that the proposed model has a distinct defect boundary delineation capability and outperforms the Mask R-CNN and the U-Net in both accuracy and speed.

深度学习技术最近在民用基础设施系统的视觉检查领域引起了极大的关注。当前，大多数基于深度学习的视觉检查技术都通过使用卷积神经网络来识别表面缺陷，方法是通过检测每个缺陷的边界框或对图像上的所有像素进行分类而不区分不同的缺陷实例。这些输出不能直接用于获取图像中每个单个缺陷的几何特性，从而阻碍了全自动结构评估技术的发展。在这项研究中，提出了一种新颖的全卷积模型，用于同时检测和分组图像上每个缺陷的图像像素。提出的模型将优化的掩码子网与框级检测网络集成在一起，其中前者输出一组用于像素级缺陷检测的位置敏感得分图，而后者预测每个缺陷的边界框以对检测到的像素进行分组。包含三种常见类型的混凝土缺陷（裂缝，剥落和裸露钢筋）的图像数据集用于模型的训练和测试。结果表明，所提出的模型对各种缺陷尺寸和形状均具有鲁棒性，并且可以实现掩模级平均平均精度（用于模型的训练和测试。结果表明，所提出的模型对各种缺陷尺寸和形状均具有鲁棒性，并且可以实现掩模级平均平均精度（用于模型的训练和测试。结果表明，所提出的模型对各种缺陷尺寸和形状均具有鲁棒性，并且可以实现掩模级平均平均精度（在NVIDIA GeForce GTX 1060 GPU上测试时，输入图像大小为576×576时，mAP）为82.4％，平均联合相交（mIoU）为75.5％，处理速度约为10 FPS。将其性能与最新的实例分割网络Mask R-CNN和语义分割网络U-Net进行比较。比较研究表明，所提出的模型具有明显的缺陷边界描绘能力，并且在准确性和速度上均优于Mask R-CNN和U-Net。