# Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN
###### Abstract
Target detection consists of extracting the moving foreground or the target of interest from the video or image taken. The objective is to detect the target's position and category simultaneously. In general, semantic segmentation requires decoding the image to the pixel domain completely, and then target detection is carried out according to the information of each pixel. Instance segmentation is to locate and classify each decoded pixel domain on the basis of semantic segmentation by a rectangular detection frame. In recent years, when researchers use deep learning techniques to perform computer vision tasks, excellent results are obtained, and certain typical deep learning models have been applied to image recognition and classification in several industries, such as crop pest identification [13], medical lesion detection and identification [14], autonomous driving [15], robotics [16] and air quality prediction [17]. The faster region-based convolutional neural networks (Faster R-CNNs), single shot multibox detector (SSD), You Only Look Once (YOLO), U-networks (U-net) and Mask R-CNN are the typical deep learning models. In these models, Faster R-CNN, SSD and YOLO can only perform target detection, U-Net can only perform semantic segmentation and Mask R-CNN is the most popular algorithm for instance segmentation at present. Mask R-CNN adds a mask branch for semantic segmentation on the basis of Faster R-CNN, fully convolutional network (FCN) is used for semantic segmentation for each proposal box of Faster R-CNN and segmentation, localization and classification tasks are carried out simultaneously. In civil engineering, these models are also widely utilized for the target detection of the defect images of the important buildings, such as cracks in high-speed railway slab tracks [18], cracks and exposed bars on the surface of tunnel walls [19], cracks on the surface of the high-rise bridge pier and cracks [20], steel corrosion and loosened bolts in steel bridges [21]. Concrete cracks were successfully detected by the Faster R-CNN based model, and a bounding box was generated to locate the cracks [22]. The improved network model based on YOLO was also successful in achieving the target detection of various concrete defects, such as cracks on concrete surfaces [23,24], spots, exposed bars and spalling [25]. In the literature [26], U-Net was used to achieve pixel-level segmentation of concrete cracks; it output a black and white image where the white areas represent the background and black areas represent the cracks. However, these research results do not achieve instance segmentation of the defects, just only target detection and semantic segmentation. Instance segmentation, as a combination of target detection and semantic segmentation, not only pinpoints the edge of the defect but also labels different parts with the same kind of defect in the image. Inliterature [27] and literature [28; 29], concrete voids and cracks were segmented based on Mask R-CNN, respectively, but both studies only address one kind of defect. In addition, researchers have cut the images before performing concrete defect identification, only one or two types of defects were retained in one image and there is artificial interference [30]. However, in original defect images, the size and shape of each defect are different and different types of defects may appear in one image, so it is difficult to achieve instance segmentation and takes a lot of time. ResNet101, the backbone network of Mask R-CNN which has too many convolution layers and only one feature layer, has a lower ability to extract features from smaller defects, and the large number of parameters leads to the slow training speed of the model. In order to obtain higher identification accuracy and better segmentation performance for concrete defects, the main work of this paper is as follows:  
1. [label=(0)]
2. Constructed the dataset from multiple defects of concrete.