Image-Based Bridge Defect Detection and Monitoring Technologies

Computer-vision is an emerging technology which has been widely used in the field of structural health monitoring. This seminar will cover the latest achievements of our research group in defect detection, health monitoring, and construction control using image-based technologies. For defect detection, advanced unmanned aerial vehicles (UAVs) have been developed to automatically detect surface cracks and other types of structural damages based on digital image processing techniques. An integrated navigation system combining binocular camera and inertial sensor enables automated route planning for the developed UAVs in case of GPS failure. Specifically, a wall-climbing UAV is designed to acquire detailed surface crack images with high accuracy and a collision-tolerant UAV is proposed for the defect inspection of complex or internal spaces. In addition to UAV, an unmanned ship is developed for exploration of inaccessible places such as sewers. For structural health monitoring, on-line camera monitoring system has been developed for displacement measurement of long-span bridges. For construction control, a binocular vision-based method is investigated and applied for displacement measurement during the hoisting process of prefabricated components. Experimental results show that compared to conventional methods, the proposed approach is more intelligent, more convenient, and more reliable

Automatic Detection of Road Cracks using EfficientNet with Residual U-Net-based Segmentation and YOLOv5-based Detection

The main factor affecting road performance is pavement damage. One of the difficulties in maintaining roads is pavement cracking. Credible and reliable inspection of heritage structural health relies heavily on crack detection on road surfaces. To achieve intelligent operation and maintenance, intelligent crack detection is essential to traffic safety. The detection of road pavement cracks using computer vision has gained popularity in recent years. Recent technological breakthroughs in general deep learning algorithms have resulted in improved results in the discipline of crack detection. In this paper, two techniques for object identification and segmentation are proposed. The EfficientNet with residual U-Net technique is suggested for segmentation, while the YOLO v5 algorithm is offered for crack detection. To correctly separate the pavement cracks, a crack segmentation network is used. Road crack identification and segmentation accuracy were enhanced by optimising the model's hyperparameters and increasing the feature extraction structure. The suggested algorithm's performance is compared to state-of-the-art algorithms. The suggested work achieves 99.35% accuracy

Classification, Localization, and Quantification of Structural Damage in Concrete Structures using Convolutional Neural Networks

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently become of great interest owing to their superior ability to detect damage in engineering structures. ML algorithms used in this domain are classified into two major subfields: vibration-based and image-based SHM. Traditional condition survey techniques based on visual inspection have been the most widely used for monitoring concrete structures in service. Inspectors visually evaluate defects based on experience and engineering judgment. However, this process is subjective, time-consuming, and hampered by difficult access to numerous parts of complex structures. Accordingly, the present study proposes a nearly automated inspection model based on image processing, signal processing, and deep learning for detecting defects and identifying damage locations in typically inaccessible areas of concrete structures. The work conducted in this thesis achieved excellent damage localization and classification performance and could offer a nearly automated inspection platform for the colossal backlog of ageing civil engineering structures