# Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN
###### Abstract  
With the rapid development of artificial intelligence, computer vision techniques have been successfully applied to concrete defect diagnosis in bridge structural health monitoring. To enhance the accuracy of identifying the location and type of concrete defects (cracks, exposed bars, spalling, efflorescence and voids), this paper proposes improvements to the existing Mask Region Convolution Neural Network (Mask R-CNN). The improvements are as follows: (i) The residual network (ResNet101), the backbone network of Mask R-CNN which has too many convolution layers, is replaced by the lightweight network MobileNetV2. This can solve the problem that the large number of parameters leads to a slow training speed of the model, and improve the ability to extract features of smaller targets. (ii) Embedding attention mechanism modules in Feature Pyramid Networks (FPNs) to better extract the target features. (iii) A path aggregation network (PANet) is added to solve the problem that the model Mask R-CNN lacks the ability to extract shallow layer feature information. To validate the superiority of the proposed improved Mask R-CNN, the multi-class concrete defect image dataset was constructed, and using the K-means clustering algorithm to determine the aspect ratio of the most suitable prior bounding box for the dataset. Following, the identification results of improved Mask-RCNN, original Mask-RCNN and other mainstream deep learning networks on five types of concrete defects (cracks, exposed bars, spalling, efflorescence and voids) in the dataset were compared. Finally, the intelligent identification system for concrete defects has been established by innovatively combining images taken by unmanned aerial vehicles (UAVs) with our improved defect identification model. The reinforced concrete bridge defects images collected by UAVs were used as test set for testing. The result is the improved Mask R-CNN with superior accuracy, and the identification accuracy is higher than the original Mask-RCNN and other deep learning networks. The improved Mask-RCNN can identify the new untrained concrete defects images taken by UAVs, and the identification accuracy can meet the requirements of bridge structural health monitoring. structural health monitoring; intelligent diagnosis; concrete defects; deep learning; Mask R-CNN +
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classify them. However, during inspections of roads, tunnel walls, train plate rails and other structures, the road must be closed, as it will have a bad effect on the traffic. When the inspectors work, they must take a special detection vehicle to reach the vicinity of the defect, but there are still some defect locations that cannot be reached. At this time, some researchers have used numerical simulation techniques to assess the residual capacity of the concrete elements in question [4], such as the innovative discrete element method [5,6] and the traditional finite element method [7]. However, only the overall bearing capacity of the structure can be assessed and subtle concrete defects cannot be detected. The use of UAVs for the inspection of building surface defects solves these problems [8,9]; when inspectors remotely operate UAVs to photograph the defects, it is both safe and fast. However, the inspectors still need to classify the recorded images, which takes a lot of time. Researchers have started to use image processing techniques for the segmentation and recognition of structural defect images [10,11]. With the rapid development of computer technology, people have begun to explore how to use computer technology to interpret images, which is referred to as computer vision technology [12]. Computer vision technology simulates biological vision through computers and related equipment, and then determines the target category. Computer vision tasks mainly include image classification, target detection, semantic segmentation and instance segmentation. Image classification is to analyze an input image and return the label task of a category contained in the image.