Hui Zuo

Accurate crack width measurement is essential for the condition assessment of reinforced concrete (RC) bridges, yet most unmanned aerial vehicle (UAV) inspections remain limited to pixel-level observations that cannot be converted into reliable metric units without an external scale reference. This paper presents a lightweight, drone-agnostic UAV-mounted metrology system that enables standards-aligned metric crack width measurement directly from inspection imagery. The payload integrates a focusable diffractive optical element (DOE) red laser that projects a cross pattern of known angular geometry, three TF-Luna time-of-flight (ToF) distance sensors, and an ESP-WROOM-32 microcontroller that provides dual-rate sampling, Bluetooth Low Energy (BLE) streaming, and on-board logging. A two-stage calibration links the synchronized distance measurements to the physical length of the projected cross, yielding an image-specific pixel-to-millimeter scale that is applied to pixel-level crack widths obtained from a vision-based segmentation pipeline. The system is field-deployed on the Puente Huamani Bridge in Pisco, Peru, where measurements of 39 cracks classified under AASHTO MBEI condition states are compared against independent manual measurements by six inspectors. The proposed system reduces measurement variability across all condition states (CS), lowering the average coefficient of variation from 0.36 to 0.10 for fine CS1 cracks, from 0.27 to 0.11 for CS2, and from 0.22 to 0.07 for CS3. Cross-platform adaptability is demonstrated through an additional deployment on a DJI Matrice 350 RTK at the Low Level Bridge in Edmonton, Canada. The results indicate that the system provides a practical, low-cost, and scalable solution for repeatable, standards-aligned UAV-based bridge crack assessment.

As aging infrastructure presents increasing safety and maintenance challenges, effective monitoring of structural defect propagation is vital. While many methods detect cracks, few can track the same crack over time under varying conditions. This study presents a modular, automated framework to track crack propagation by integrating scale‐invariant feature transform (SIFT)–based image matching, Mask2Former segmentation, and skeleton‐based morphological analysis. SIFT identifies matched keypoints to retrieve relevant image pairs across inspections, Mask2Former with a Swin‐T backbone delivers superior segmentation accuracy, while a custom postprocessing pipeline quantifies crack width, length, and area using skeletonization and a branch‐pruning algorithm. These geometric features are compared over time to assess crack growth. Validation on ultra‐high‐performance concrete (UHPC) beam tests demonstrates robustness to variations in illumination, scale, and rotation, high segmentation performance (81.60% mIoU and 88.84% mAcc), and measurement error within 1 mm. The framework can support long‐term defect monitoring and enable consistent, data‐driven maintenance strategies in structural health monitoring.

This study proposes a multi-resolution fast 3D reconstruction framework that integrates transformer-based damage detection with rapid 3D modeling to enhance bridge surface defect identification and spatial localization. The framework consists of three phases: (I) 3D reconstruction using Structure from Motion (SfM) to generate a structural model with sparse point cloud, (II) damage segmentation via a customized Swin UNETR model for precise defect detection, and (III) multi-resolution dense reconstruction that prioritizes high-resolution modeling of detected defects while reducing the resolution of non-critical areas to improve efficiency. Experimental validation on the High Level Bridge in Edmonton, Canada, demonstrated the framework's capability to accurately map surface defects onto a 3D model, providing an intuitive and detailed localization for structural assessment. This approach offers significant potential for efficient and accurate bridge inspection, supporting data-driven maintenance strategies.

Transrectal ultrasound prostate biopsy is the most commonly used method for the diagnosis of prostate cancer. During the operation, the doctor needs to manually adjust the ultrasound probe for repeated adjustments, which is difficult to ensure the efficiency, accuracy, and safety of the operation. This paper presents a passive posture adjusting mechanism of transrectal ultrasound probe. The overall mechanism has 7 degrees of freedom, consisting of a position adjustment module, a posture adjustment module, and an ultrasonic probe rotation and feed module. In order to achieve the centering function, the posture adjustment module is designed based on the double parallelogram. Centering performance is verified based on SimMechanics, and remote center point error of physical prototypes is evaluated. The maximum error of the azimuth remote center point motion and the maximum error of the remote center point motion of the ultrasonic probe are 4 mm and 3.4 mm, respectively, which are less than the anus that can withstand 6 mm. Meanwhile, the analysis of measurement error shows that the random error correlation is weak in different directions, the systematic error confidence intervals of azimuth and elevation angle are less than 2.5 mm, and the maximum relative fixed point error and the maximum relative standard error are 14.73% and 14.98%, respectively. The simulation and testing results have shown the effectiveness and reliability of the propose mechanism.