Search Results (145)

Non-contact metric inspection of metallic circular holes is essential for assembly quality control, yet remains difficult on reflective machined surfaces, where depth scale, stripe radiometry, and contour geometry degrade simultaneously. Pure monocular two-dimensional vision localizes hole boundaries efficiently but cannot resolve metric depth; multi-camera three-dimensional systems remove this ambiguity but with a heavier hardware and calibration cost; and conventional structured-light pipelines often improve stripe extraction or circle fitting in isolation, leaving the overall measurement chain fragile when reflection and edge defects co-occur. This paper proposes a monocular structured-light framework that treats sensing geometry, radiometric stripe reliability, and outlier-robust hole estimation as one coupled measurement chain. A single calibrated industrial camera is combined with an obliquely projected line laser to fuse top-view contour observation with light-plane-constrained depth recovery. Multi-exposure high-dynamic-range (HDR) fusion, adaptive Gaussian regularization, and distance-weighted gray-centroid refinement stabilize sub-pixel stripe centerlines under local saturation and uneven illumination, while geometry-aware contour screening and probabilistic multi-stage RANSAC fitting suppress burr-induced outliers during circle-parameter estimation. Experiments were conducted on a steel bolt-hole workpiece and a 6061 aluminum-alloy plate containing five holes with nominal diameters spanning approximately 30–78 mm. The original steel workpiece was evaluated for its diameter and two datum-related center-position quantities, while the five-hole plate was evaluated through three repeated optical diameter measurements and independent CMM references. For the five aluminum-alloy holes, the mean optical–CMM diameter differences ranged from 0.006 to 0.218 mm, with an average absolute difference of 0.096 mm. The results establish feasibility over the tested workpieces and calibrated measurement volume rather than generalization to arbitrary hole geometries, materials, or surface conditions. Full article

To address the difficulty in growth point localization caused by cotyledon overlapping and leaf occlusion during plug seedling grafting, a localization method based on grid structured light is proposed. An acquisition system consisting of a complementary metal-oxide-semiconductor (CMOS) camera and a grid structured light laser projector is established. A multi-depth plane calibration method is adopted to fit the light plane equation for each laser line. To address the grid line discontinuity problem, a coding method based on three-dimensional constraints of light planes is proposed. The cotyledon point cloud is reconstructed by combining the light plane equations with the camera model, and a circumscribed triangle is constructed to approximate the arc center of the fan-shaped point cloud for growth point localization. Experimental results show that the average interlayer error of light plane calibration is 0.059 mm. For 50 non-overlapping single seedlings, the average localization error is 1.68 mm with a success rate of 100%. For 150 overlapping seedlings, the success rate reaches 89%, outperforming the traditional ellipse fitting method (68%). The proposed method can provide reliable growth point localization information for grafting robots. Full article

Small surface defects on large structures such as wind turbine blades, bridges, and pipelines pose significant safety risks if left undetected. Therefore, a laser triangulation system is designed for contactless surface geometry inspection from a flying drone at a working distance of 2 m. To enable single-shot triangulation measurements in dynamic aerial environments, a single-shot-capable approach is realized by means of a laser and a diffractive optical element for creating a dot-matrix illumination pattern and a camera for image recording. The setup, with 101 × 101 measurement points, is calibrated by using an interferometer as a reference, which shows a sub-pixel resolution capability. As a result, the depth resolution capability for each point amounts to 126 µm, while the lateral resolution capability is determined by the laser spots’ size of 0.6 mm and the spots’ interspacing of 1.75 mm. With the present configuration, unambiguous depth detection is possible for local surface gradients of up to 2.3 times the interspot distance between adjacent measurement points, and the field of view is 17.56 cm × 17.56 cm. Finally, surface defects with lateral sizes on the order of 1 cm and 0.5 cm are currently detectable, as is demonstrated by experimental results from in-flight measurements. Thus, the potential and challenges of single-shot laser triangulation for drone-based inspection in real-world scenarios are presented. Full article

To address the multi-sensor collaborative calibration challenges in laser 3D projection systems, a pose calibration method integrating binocular vision and laser ranging is proposed. A multi-coordinate system fusion framework encompassing the camera coordinate system, galvanometer coordinate system, and workpiece coordinate system is established. Through the calculation of reference pose matrices and real-time transformations, adaptive calibration under arbitrary workpiece placements is achieved. Experimental results demonstrate that within a working range of 1.5–2.5 m, the calibration error is 45.5 $\mathsf{\mu }$m, meeting the high-precision requirements of aerospace precision machining and assembly. Full article

Laser powder bed fusion (LPBF) is susceptible to defects arising from melt pool instabilities, spatter, heat accumulation, and powder spreading anomalies. In situ infrared (IR) monitoring can detect these issues; however, it typically generates large volumes of data that are costly to store and analyze. This work proposes a projection-based framework that directly maps in situ thermal measurements onto a three-dimensional (3D) voxelized part geometry, substantially reducing storage requirements while preserving spatial fidelity. In addition, several IR derived features are incorporated into a practical workflow for defect detection and process model calibration, including laser scan order, local pre-deposition temperature, maximum pre-scan temperature, and spatter generation and landing locations. For completeness, commonly used metrics such as interpass temperature, heat intensity, cooling rate, and relative melt pool area are extracted within the same unified processing pipeline. All features are computed using a consistent, reproducible Python-based implementation to streamline integration into routine monitoring and analysis tasks. Multiple parts are fabricated, monitored, and characterized to evaluate the proposed framework, demonstrating that the extracted features reliably identify process anomalies and correlate with observed defects. Full article

A galvanometer-based laser 3D projection system requires accurate mapping between galvanometer control signals and workpiece coordinates to ensure reliable on-part marking. This study presents a calibration and verification pipeline that uses a color camera and a depth sensor to reconstruct 3D target points and estimate the extrinsic parameters between the projector and the workpiece. Laser spot centers are localized in color images, and corresponding depth values are acquired after color–depth alignment. The resulting 3D points are back-projected and transformed into the workpiece coordinate frame. A hybrid solver is employed: the Whale Optimization Algorithm (WOA) provides a global initial estimate, followed by Levenberg–Marquardt (LM) refinement to enhance convergence stability under noisy and small-sample conditions. Experimental validation on an independent 13-point set demonstrates sub-millimeter accuracy, with a mean error of approximately 0.37 mm and a maximum error of 0.87 mm. A further rectangular contour projection test confirms consistent performance, yielding a mean error of 0.434 mm and a maximum error of 0.879 mm, with all errors remaining below 1 mm. Full article

Reliable positioning performance is crucial in precision industrial automation, especially under dynamic conditions. This research focuses on examining the accuracy of a toothed belt driven linear servo motor positioning system, with the aim of identifying the main factors influencing position deviation. The system was built on a Power Belt ITO 060M shaft, controlled by an Rtelligent RS200-G servo controller and an Omron CP1L-E PLC. Position measurement was performed by a laser distance meter and a Cognex IS2000C-130-40-SR8 industrial camera, both calibrated with certified gauge blocks. The linear unit was moved to predefined points at different speeds, accelerations, and decelerations profiles and the resulting position deviation was recorded for each case. Several analytical methods were used to evaluate the collected measurement data to determine which factors have the greatest impact on positioning error. The result showed that speed significantly affected the accuracy of the system, while the effects of deceleration and acceleration were less pronounced. The study contributes to the fine-tuning of linear motion system and the targeted improvement of their performance. Full article

The calibration of levelling staff is a key prerequisite for achieving high-precision levelling. Traditionally, this process is carried out using laser interferometric systems, which provide the required accuracy but are demanding in terms of operation, maintenance, and measurement conditions. This paper focuses on verifying the applicability of the convergent photogrammetry method for levelling staff calibration with a target accuracy of 0.010 mm. An experimental prototype of a photogrammetric calibration system (without real scale) was developed and tested using three different lenses, two processing software packages (Photomodeler and Agisoft Metashape), and two different approaches to camera calibration (self-calibration and field calibration). The repeatability of measurements was evaluated based on mutual lengths between selected checkpoints and the accuracy of determining the 3D positions of these points. The results showed that the Nikon AF-S NIKKOR 35 mm f/1.8G ED lens achieved the best repeatability and met the target accuracy requirement, while Photomodeler yielded smaller standard deviations in the determination of control point positions compared to Agisoft Metashape. The findings indicate that convergent photogrammetry, when applied under optimal conditions, has the potential to achieve the accuracy required for high-precision measurements in metrology, and may even offer an alternative to laser interferometric calibration systems in certain applications. Full article

In this study, we present a non-invasive and contactless method for estimating the internal temperature of Cr:LiCAF laser crystals using temperature-dependent shifts in fluorescence emission peaks. A high-resolution calibration dataset was created with 181 spectral points from 10 to 100 °C. Linear regression yielded a temperature estimation model with an R² of 0.73, which was validated under both lasing and non-lasing conditions. To further evaluate the reliability of this optical thermometry method, thermal imaging data from a FLIR E75 infrared camera were incorporated. Surface temperatures measured at various diode current levels closely matched the internal temperature predictions based on fluorescence shifts (MAE = 0.775 °C, R² = 0.993), confirming the robustness of the method. This dual-approach validation enhances confidence in using fluorescence-based diagnostics for real-time thermal monitoring in laser systems. The combined use of spectrometer-based and thermal camera measurements suggests potential for hybrid diagnostics in laser research and development, offering improved thermal feedback for optimizing high-power laser performance. Full article

This research presents a novel, low-cost optical acquisition system based on infrared imaging for real-time weld bead geometry monitoring in Gas Metal Arc Welding (GMAW). The system uniquely employs a commercial CCD camera (1000–1150 nm) with tailored filters and lenses to isolate molten pool thermal radiation while mitigating arc interference. A single camera and a mirror-based setup simultaneously capture weld bead width and reinforcement. Acquired images are processed in real time (10 ms intervals) using MATLAB R2016b algorithms for edge segmentation and geometric parameter extraction. Dimensional accuracy under different welding parameters was ensured through camera calibration modeling. Validation across 35 experimental trials (over 6000 datapoints) using laser profilometry and manual measurements showed errors below 1%. The resulting dataset successfully trained a Support Vector Machine, highlighting the system’s potential for smart manufacturing and predictive modeling. This study demonstrates the viability of high-precision, low-cost weld monitoring for enhanced real-time control and automation in welding applications. Full article

Precise online measurement of large structural components is urgently needed in modern manufacturing and intelligent construction, requiring a measurement range over 1 m, near-millimeter accuracy, second-level measurement speed, and adaptability to complex environments. In this paper, three mainstream measurement technologies, namely the image method, line laser scanning method, and structured light method, are comparatively analyzed. The structured light method exhibits remarkable comprehensive advantages in terms of accuracy and speed; however, it suffers from the issue of occlusion during contour measurement. To tackle this problem, multi-camera stitching is employed, wherein the accuracy of camera calibration plays a crucial role in determining the quality of point cloud stitching. Focusing on the cable tightening scenario of meter-diameter cables in cable-stayed bridges, this study develops a contour measurement system based on the collaboration of multiple structured light cameras. Measurement indicators are optimized through modeling analysis, system construction, and performance verification. During verification, four structured light scanners were adopted, and measurements were repeated 11 times for the test workpieces. Experimental results demonstrate that although the current measurement errors have not yet been stably controlled within the millimeter level, this research provides technical exploration and practical experience for high-precision measurement in the field of intelligent construction, thus laying a solid foundation for subsequent accuracy improvement. Full article

Underwater Optical Camera Communication (UOCC) has emerged as a promising paradigm for short-range, high-bandwidth, and secure data exchange in autonomous underwater vehicles (AUVs). UOCC performance strongly depends on exposure time and International Standards Organization (ISO) sensitivity—two parameters that govern photon capture, contrast, and bit detection fidelity. However, optical propagation in aquatic environments is highly susceptible to turbidity, scattering, and illumination variability, which severely degrade image clarity and signal-to-noise ratio (SNR). Conventional systems with fixed imaging settings cannot adapt to time-varying conditions, limiting communication reliability. While validating the feasibility of deep learning for exposure prediction, this baseline lacked environmental awareness and generalization to dynamic scenarios. To overcome these limitations, we introduce a Real-to-Sim-to-Deployment framework that couples a physically calibrated emulation platform with a Hybrid CNN-MLP Model (HCMM). By fusing optical images, environmental states, and camera configurations, the HCMM achieves substantially improved parameter prediction accuracy, reducing RMSE to 0.23–0.33. When deployed on embedded hardware, it enables real-time adaptive reconfiguration and delivers up to 8.5 dB SNR gain, surpassing both static-parameter systems and the prior CNN baseline. These results demonstrate that environment-aware multimodal learning, supported by reproducible optical channel emulation, provides a scalable and robust solution for practical UOCC deployment in positioning, inspection, and laser-based underwater communication. Full article

This study proposes a novel structural deflection measurement method using a single smartphone with an innovative scale factor (SF) calibration technique, eliminating reliance on laser rangefinders and industrial cameras. Conventional off-axis digital image correlation (DIC) techniques require laser rangefinders to measure discrete points for SF calculation, suffering from high hardware costs and sunlight-induced ranging failures. The proposed approach replaces physical ranging by deriving SF through geometric relationships of known structural dimensions (e.g., bridge length/width) within the measured plane. A key innovation lies in developing a versatile SF calibration framework adaptable to varying numbers of reference dimensions: a non-optimized calculation integrates smartphone gyroscope-measured 3D angles when only one dimension is available; a local optimization model with angular parameters enhances accuracy for 2–3 known dimensions; and a global optimization model employing spatial constraints achieves precise SF resolution with ≥4 reference dimensions. Indoor experiments demonstrated sub-0.05 m ranging accuracy and deflection errors below 0.30 mm. Field validations on Beijing Subway Line 13′s bridge successfully captured dynamic load-induced deformations, confirming outdoor applicability. This smartphone-based method reduces costs compared to traditional setups while overcoming sunlight interference, establishing a hardware-adaptive solution for vision-based structural health monitoring. Full article

This study presents a high-precision underwater 3D reconstruction system based on rotating laser scanning. The system adopts a modeling framework that combines air-based camera calibration with refraction compensation. A five-stage image preprocessing pipeline, followed by an adaptive locally weighted centroid (ALWC) algorithm, is employed to extract the laser stripe centerline with high accuracy. In addition, a synchronized feature extraction-based joint calibration method is introduced to simultaneously calibrate the light plane and the rotation axis. Resolution evaluation experiments demonstrate that the system achieves a spatial resolution of better than 0.6 mm in clear water at a distance of 1 m, and approximately 0.8 mm in turbid water conditions of 6 Nephelometric Turbidity Units (NTU), thereby verifying its reconstruction accuracy and robustness under varying water clarity. Full article

This paper presents a non-intrusive method for estimating the distance between a camera and a human subject using a monocular vision system and statistically derived interpupillary distance (IPD) values. The proposed approach eliminates the need for individual calibration by utilizing average IPD values based on biological sex, enabling accurate, scalable distance estimation for diverse users. The algorithm, implemented in Python 3.12.11 using the MediaPipe Face Mesh framework, extracts pupil coordinates from facial images and calculates IPD in pixels. A sixth-degree polynomial calibration function, derived from controlled experiments using a uniaxial displacement system, maps pixel-based IPD to real-world distances across three intervals (20–80 cm, 80–160 cm, and 160–240 cm). Additionally, a geometric correction is applied to compensate for in-plane facial rotation. Experimental validation with 26 participants (15 males, 11 females) demonstrates the method’s robustness and accuracy, as confirmed by relative error analysis against ground truth measurements obtained with a Bosch GLM120C laser distance meter. Males exhibited lower relative errors across the intervals (3.87%, 4.75%, and 5.53%), while females recorded higher mean relative errors (6.0%, 6.7%, and 7.27%). The results confirm the feasibility of the proposed method for real-time applications in human–computer interaction, augmented reality, and camera-based proximity sensing. Full article