Bolted connections are essential components that require regular inspection to ensure bridge safety. Existing methods mainly rely on traditional artificial vision-based inspection, which is inefficient due to the many bolts of bridges. A vision-based method using deep learning and unmanned aerial vision is proposed to automatically analyze the bridge bolts’ condition. The contributions are as follows: (1) Addressing the problems that motion blur often exists in videos captured by unmanned ariel systems (UASs) with high moving speed, and that bolt damage is hard to accurately detect due to the few pixels a single bolt occupies, a bolt image preprocessing method, including image deblurring based on inverse filtering with camera motion model and adaptive scaling based on super-resolution, is proposed to eliminate the motion blur of bolt images and segment them into subimages with uniform bolt size. (2) Addressing the problem that directly applying an object detection network for both bolt detection and classification may lead to the wrong identification of bolt damage, a two-stage detection method is proposed to divide bolt inspection into bolt object segmentation and damage classification. The proposed method was verified on an in-service bridge to detect bolts and classify them into normal bolts, corrosion bolts, and loose bolts. The results show that the proposed method can effectively eliminate the inherent defects of data acquired by UAS and accurately classify the bolt defects, verifying the practicability and high precision of the proposed method.

Damage inspection on the undersides of bridges is an important and challenging part of routine bridge inspections. A method for 3D reconstruction and damage localization of bridge undersides based on close-range photography by unmanned aerial vehicle (UAV) and stereo vision combined with deep learning algorithms is proposed, the specific contributions include: (1) proposing a close-range photography method for acquiring high-resolution images from multiple perspectives of the bridge underside by UAVs, serving as the data source for damage analysis; (2) applying a deep learning-assisted segmentation method to optimize the multi-view geometry-based 3D reconstruction method, improving the efficiency of three-dimensional reconstruction, and defining the projection direction from the 3D reconstruction results to obtain ultra-high-resolution panoramic images of the bridge underside; (3) addressing the issue of detecting minor damages in large panoramic images by using a slice-assisted reasoning module and a lightweight convolutional YOLO v8 network to identify exposed steel bars corroded due to concrete damage in the panoramic images, and defining a coordinate system to localize the damages on the bridge underside. The proposed method was applied to damage detection and localization on the underside of a 160 m span main span of an in-service concrete bridge. The results demonstrate that the proposed method can quickly and accurately identify exposed steel bar corrosion on the bridge underside and output reports, proving the practicality of the proposed method.

Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection method for bridge surface damage based on Unmanned Aerial Vehicles (UAVs) is proposed for these challenges, incorporating a three-stage detection, quantification, and visualization process. This method enables automatic crack detection, quantification, and localization in a 3D model, generating a bridge model that includes crack details and distribution. The key contributions of this method are as follows: (1) The DCN-BiFPN-EMA-YOLO (DBE-YOLO) crack detection network is introduced, which improves the model’s ability to extract crack features from complex backgrounds and enhances its multi-scale detection capability for accurate detection; (2) a more comprehensive crack quantification method is proposed, integrating the crack automation detection system for accurate crack quantification and efficient processing; (3) crack information is mapped onto the 3D model by computing the camera pose for each image in the 3D model for intuitive crack visualization. Experimental results from tests on a concrete beam and an urban bridge demonstrate that the proposed method accurately identifies and quantifies crack images captured by UAVs. The DBE-YOLO network achieves an accuracy of 96.79% and an F1 score of 88.51%, improving accuracy by 3.19% and the F1 score by 3.8% compared to the original model. The quantification accuracy is within 10% of the error margin of traditional manual inspection. A 3D bridge model was also constructed and integrated with crack information.

Rapid inspection of urban road cracks is vital to maintain traffic smoothness and ensure traffic safety. A rapid pavement crack inspection method uses low-altitude aerial images captured by an unmanned aerial system (UAS) and deep-learning aided 3D reconstruction, and a learning-based object segmentation algorithm is proposed to measure road cracks automatically. The contributions include: (a) An efficient 3D reconstruction method for low-altitude aerial images captured by a UAS is proposed, which applies an instance segmentation network to segment road targets from raw images with complex backgrounds first and then performs structure from motion to reconstruct a large-scale road orthophoto from a large number of aerial images. (b) To detect cracks from the reconstructed large-size road orthophoto, a sliding window algorithm and U-Net model optimized with a transformer structure are used to automatically identify and segment the cracks from the orthophoto at the pixel level. Then, a connected domain feature analysis method is used to measure the road crack length. The proposed method is applied to detection of road cracks in a 1.5 km² area of a city. The results show that the proposed method can effectively and accurately detect cracks and measure the length of cracks in the 4-km-long road, which proves the practicality of the proposed method.

Bridge deformation response data are the basis for calculating the dynamic parameters of the bridge, and it is of great significance to accurately measure the deformation response of the bridge during the load test and service conditions. A bridge deformation measurement method using an unmanned aerial system (UAS) with dual cameras and a deep learning-based object tracking method is proposed to measure the bridge deformation. The contributions are as follows: (1) To address the problem that the movement of the UAS brings error to the deformation measurement results, dual cameras with telephoto and wide-angle lenses are used to simultaneously capture the deformed points and stable points on the bridge, so as to simultaneously measure the deformation of the bridge and the displacement of the UAS, and then the displacement of UAS is eliminated by using the homography relationship between the two cameras. (2) To solve the problem that the traditional digital image correlation-based displacement measurement method is easily disturbed by factors such as light changes and occlusion, a displacement calculation method based on object detection network and target tracking algorithm is proposed to achieve the stable target displacement measurement. Finally, the proposed method was verified in a laboratory test and applied to the deformation measurement of an in-service bridge to verify the practicability of the proposed method.

The rapid measurement of three-dimensional bridge geometric shapes is crucial for assessing construction quality and in-service structural conditions. Existing geometric shape measurement methods predominantly rely on traditional surveying instruments, which suffer from low efficiency and are limited to sparse point sampling. This study proposes a novel framework that utilizes an airborne LiDAR–camera fusion system for data acquisition, reconstructs high-precision 3D bridge models through real-time mapping, and automatically extracts structural geometric shapes using deep learning. The main contributions include the following: (1) A synchronized LiDAR–camera fusion system integrated with an unmanned aerial vehicle (UAV) and a microprocessor was developed, enabling the flexible and large-scale acquisition of bridge images and point clouds; (2) A multi-sensor fusion mapping method coupling visual-inertial odometry (VIO) and Li-DAR-inertial odometry (LIO) was implemented to construct 3D bridge point clouds in real time robustly; and (3) An instance segmentation network-based approach was proposed to detect key structural components in images, with detected geometric shapes projected from image coordinates to 3D space using LiDAR–camera calibration parameters, addressing challenges in automated large-scale point cloud analysis. The proposed method was validated through geometric shape measurements on a concrete arch bridge. The results demonstrate that compared to the oblique photogrammetry method, the proposed approach reduces errors by 77.13%, while its detection time accounts for 4.18% of that required by a stationary laser scanner and 0.29% of that needed for oblique photogrammetry.