# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
## 1 Introduction  
Concrete material is widely used in construction engineering, such as roads, bridges, walls, and so on. The crack width on the surface of the concrete structure directly reflects its degradation degree and bearing capacity. Regular detection of cracks plays an important role in the maintenance and operation of existing infrastructure and buildings. Traditional crack measurement is mainly carried out by inspectors using crack scales or magnifying glasses, which is time-consuming, tedious, and subjective [1]. With the development of technology, crack detection systems based on fiber optic sensors, laser, stereo imaging, ultrasonic and other technologies have been developed [2; 3], but these systems are often very expensive. For roads, bridges, and other large detection areas, many institutions are unable to use these methods for regular inspection of cracks in the concrete surface, usually only one inspection a year, leading to their inability to timely evaluate the safety situation, resulting in a lot of accidents due to the deterioration of the road, and bridge deck structure. Compared with the above technology, the measurement method based on visual inspection technology has the advantages of non-contact and low hardware cost [4; 5]. Therefore, in recent years, the vision-based crack measurement method has gradually become a hot research topic. The overall process is divided into two steps: detection and coordinate transformation. Traditional image crack detection algorithms include morphological methods, edge detection methods, and statistics-based methods [6], but these methods have low detection accuracy in noisy images. At present, crack detection methods using deep learning are widely used, and their detection methods mainly include two kinds: anchor-based object detection algorithm and semantic segmentation algorithm. [PERSON] et al. [7] used Fast RCNN to extract the crack area in the panoramic image with an anchor box and processed theimage in the area to obtain the length and width of the crack. However, the noise existing in the anchor box still affected the subsequent crack boundary extraction [8; 9]. At present, many studies on crack segmentation based on semantic segmentation models such as FCN [10; 11; 12], U-Net [13; 14; 15; 16; 17], PSPNet [18], and Deeplab series [19; 20] have emerged, which verify the effectiveness of semantic segmentation models for crack extraction. However, the segmentation accuracy of these models needs to be improved. High-precision segmentation of the crack edge is helpful to reduce the image processing steps before subsequent measurement, and improve the automation level and the final measurement accuracy. As the new peak of semantic segmentation, the DeeplabV3+ model has high accuracy in most datasets. But because of the imbalance of samples in the crack dataset and the sparsity of the model's high semantic receptive field, the model often performs poorly in crack segmentation experiments. After the crack edge is accurately extracted, most studies on crack measurement based on visual pixel position have used a camera in the past, which requires that the optical axis of the camera should be perpendicular to the crack surface. When the detection distance and angle change, it needs to be re-calibrated [21; 22], which makes mobile deployment difficult. To this end, [PERSON] [23] used a single camera and a laser range finder to ensure real-time calibration of parameters, but the error will increase sharply when the angle between the camera and the object surface exceeds 50\({}^{\circ}\). In contrast, binocular vision measurement establishes the spatial relationship between the camera and the object through left-right image matching and coordinate transformation and does not need to re-calibrate when the distance and angle between the camera and the object change [24; 25; 26]. However, due to the mismatching that often occurred in the current stereo-matching algorithm, the accuracy of the edge measurement is often not high.