Computer Vision System Toolbox Matlab 2010 A Crack NEW!
Therefore, the fast acceleration of computer vision in 2010, appreciations to deep learning, and the emergence of open-source projects and large image databases only raised the market for image processing tools. Thus, many valuable libraries and projects help crack image processing concerns with machine learning or enhance the processing pipelines in computer vision projects.
Computer Vision System Toolbox Matlab 2010 A Crack
Engineering inspection and maintenance technologies play an important role in safety, operation, maintenance and management of buildings. In project construction control, supervision of engineering quality is a difficult task. To address such inspection and maintenance issues, this study presents a computer-vision-guided semi-autonomous robotic system for identification and repair of concrete cracks, and humans can make repair plans for this system. Concrete cracks are characterized through computer vision, and a crack feature database is established. Furthermore, a trajectory generation and coordinate transformation method is designed to determine the robotic execution coordinates. In addition, a knowledge base repair method is examined to make appropriate decisions on repair technology for concrete cracks, and a robotic arm is designed for crack repair. Finally, simulations and experiments are conducted, proving the feasibility of the repair method proposed. The result of this study can potentially improve the performance of on-site automatic concrete crack repair, while addressing such issues as high accident rate, low efficiency, and big loss of skilled workers.
As one of the potentially useful technologies, computer vision is increasingly implemented in automated recognition of concrete cracks (Dan & Dan, 2021). Surface condition deficiencies are often evaluated by combining computer-vision detection and surveying equipment (Shamsabadi, et al., 2022). As a result, computer-vision-based concrete-crack detection is becoming a type of non-destructive testing technique (Kim et al., 2022), with many methodologies used to determine the existence and location of cracks. Although some studies have focused on extracting such basic information as length, width, and depth (Cha et al., 2017), such information is not enough in making decisions on crack repair behavior.
A robot system for construction quality assessment has been used to optimize the autonomous visual inspection function, so as to cut labor cost and improve accuracy (Liu et al., 2017). An automated integration system has been developed for remote inspection and repair without direct human intervention. In addition, a semi-autonomous robotic system has been proposed for inspection and repair of pavements and bridges, while improving the security of properties and inspectors (Sutter et al., 2018). However, these repair platforms are semi-autonomous and pre-programmed. In contrast, this study will design a computer-vision-guided semi-autonomous robotic system for concrete crack inspection and repair, with the help of human decisions.
The purpose of crack inspection may vary, depending on parameters to be inspected. Crack detection may be delivered based on length, width, depth, and direction of cracks (Pantoja-Rosero et al., 2022). The major advantage of the computer vision technique lies in that it can provide more accurate results than traditional manual methods (Shanaka et al., 2022). Some of the conundrums in computer vision recognition are related with different shapes, irregular cracks, and various noises. By virtue of superior performance of computer vision, many types of image processing and recognition methods have been proposed, such as integrated algorithm, morphological approach, percolation-based method, and practical technique (Wang et al., 2010), most of which are used to determine whether cracks exist and where they are located. Spatial wavelet transformation has also been proposed to detect and localize cracks by amplifying weak perturbation signal at crack locations (Mardasi et al., 2018). Liu et al. (2019) have used U-net fully convolutional networks to detect concrete cracks based on computer vision. Liu et al. (2021) have adopted the integration of Convolutional Neural Network and Active Contour Model to perform crack segmentation. With deep learning in frequency domain, Zhang et al. (2020) try to detect cracks on concrete bridge decks in real time.
Several previous studies have attempted to optimize the crack recognition process for concrete values. For example, a computer vision system for a train inspection monorail was proposed and installed in the Large Hadron Collider to gather data from various sensors and capture images by the European Organization for Nuclear Research, only purposed for recording data and reducing personnel intervention (Attard et al., 2018). The recognition process of engineering concrete cracks has been automated to a certain extent based on deep learning methods (Chheng & Likitlersuang, 2018), including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transfer Learning (2017b; Cha et al., 2018; Huang et al., 2018; Xue & Li, 2018; Zhang et al., 2017a). Overall, the structural crack repair process is slow, labor intensive, and subjective so far. To overcome these working limitations, automatic repair systems have to be developed (Kovačević et al. 2021).
An urgent need is to design a fully automated integration system for inspection and reparation, which shall enable remote operations, without any need for direct human intervention. One selection is to improve the automatic behaviors of robots. It has been shown that the quality of manual operations depends not only on the experience of workers, but also on the level of their fatigue. Therefore, this system to be designed shall ensure the safety and suitability of the control mode. Being semi-autonomous, this system can improve the inspection efficiency and accuracy by automatically identifying concrete cracks. Tele-operation of robots should be considered for the operation process. In addition, a semi-supervised computer vision system has been developed in ROBO-SPECT European FP7 project to detect tunnel diseases (Menendez et al., 2018), and Harsh et al. (2020) have used robots and computer vision to detect and quantify defects in dam spillways. As these repair platforms are semi-autonomous and pre-programmed, most of the current crack inspection and repair platforms are focused only on detection. To address this limitation, this study introduces a computer-vision-guided semi-autonomous robotic system, which is dedicated to concrete crack inspection and repair projects that involve decision making by humans.
As shown in Fig. 1, four main steps are involved in the computer-vision-guided semi-autonomous concrete crack repair process using robotic arms. The first step includes feature acquisition and trajectory extraction, purposed to recognize concrete cracks. Feature acquisition is performed to determine the length, width, depth, and other measures of cracks through a computer vision process, while trajectory coordinates are calculated via hand-eye calibration. After a knowledge base is created to determine appropriate crack features for the repair method, the overall repair process will be simulated by code programming and software operation. The decision made by humans based on the knowledge base includes the establishment of relevant standards and specification databases, e.g., a crack feature database. Once the simulation process is determined, the crack repair process will be launched, including the design of robotic arms, the execution of repair operation, and the evaluation in the next step. Finally, the semi-autonomous concrete-crack repair process is tested and verified under laboratory conditions based on computer vision. Each aspect of the repair process is described in detail in Fig. 1.
The computer-vision-based crack-feature acquisition involves: detecting cracks, determining locations of crack components, and measuring the length, width, and depth of cracks. A project requires a large amount of data, which have to be recorded and organized through various methods. A database is needed to store the data on the majority of concrete crack repairs. In general, managing the raw data involves an independent database, which can be built with an electronic spreadsheet application. Crack features can be divided into eight categories based on the following engineering feature values: component position, crack position, crack material, crack properties, crack width, crack length, crack depth, and crack direction, as shown in Table 1.
Various features are detected with different techniques. For instance, some crack features are acquired in infrared, laser, ultrasonic, and various other computer-vision-based imaging methods. With regard to the infrastructure for the crack feature acquisition using the computer vision technique, a general workflow of such acquisition is shown in Fig. 2.
Given the expanding ability of robots to take semi-autonomous concrete crack repair, it is imperative that mechanisms are put in place to guarantee the safety of their behavior and process simulation. Moreover, semi-autonomous robots should be safer; arguably, they should also be explicitly executable. By using the RobotStudio design module, a virtual simulation environment is designed, where a robot arm and concrete cracks can be displayed. First, the robot or robotic platform for repairing concrete cracks can be arranged. Next, the execution process can be simulated to detect the movement conflict by using a collision detection module and code compilation. It is demonstrated that the simulation should enable the robot to prevent human from being harmed in simple test scenarios and make the repair process more efficient and feasible. The semi-autonomous robot arm provides software for offline and online programming of robots. It implements a methodology to create a BIM model of an existing physical robot, which is described by taking the example of 6-axis robotic manipulator (ABB IRB 6700-235). Later, the crack trajectory parameters extracted with computer vision are compiled to execute the code. With the locus coordinate parameters, action simulation is exported. The simulation is developed with RobotStudio, which can connect to Visual Studio to execute the robot motion and collision detection. The application is finally integrated with the robotic manipulator, as shown in Fig. 10.