Search Results (93)

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

The 3D point cloud generated by MVSNet has good scene integrity but lacks sensitivity to details, causing holes and non-dense areas in flat and weak-texture regions. To address this problem and enhance the point cloud information of weak-texture areas, the BCA-MVSNet network is proposed in this paper. The network integrates the Bidirectional Feature Pyramid Network (BIFPN) into the feature processing of the MVSNet backbone network to accurately extract the features of weak-texture regions. In the feature map fusion stage, the Coordinate Attention (CA) mechanism is introduced into 3DU-Net to obtain the position information on the channel dimension related to the direction, improve the detail feature extraction, optimize the depth map and improve the depth accuracy. The experimental results show that BCA-MVSNet not only improves the accuracy of detail texture reconstruction, but also effectively controls the computational overhead. In the DTU dataset, the Overall and Comp metrics of BCA-MVSNet are reduced by 10.2% and 2.6%, respectively; in the Tanksand Temples dataset, the Mean metrics of the eight scenarios are improved by 6.51%. Three scenes are shot by binocular camera, and the reconstruction quality is excellent in the weak-texture area by combining the camera parameters and the BCA-MVSNet model. Full article

Aiming at the problems of visual occlusion, reduced positioning accuracy and pose loss in the dynamic scanning process of aviation large components, this paper proposes a binocular vision dynamic positioning method based on magnetic target. This method detects the spatial coordinates of the magnetic target in real time through the binocular camera, extracts the target center to construct a unified reference system of the measurement platform, and uses MATLAB simulation to analyze the influence of different target layouts on the scanning stability and positioning accuracy. On this basis, a dual-objective optimization model with the objectives of ‘minimizing the number of targets’ and ‘spatial distribution uniformity’ is established, and Monte Carlo simulation is used to evaluate the robustness under Gaussian noise and random frame loss interference. The experimental results on the C-Track optical tracking platform show that the optimized magnetic target layout reduces the rotation error of the dynamic scanning from 0.055° to 0.035°, the translation error from 0.31 mm to 0.162 mm, and the scanning efficiency is increased by 33%, which significantly improves the positioning accuracy and tracking stability of the system under complex working conditions. This method provides an effective solution for high-precision dynamic measurement of aviation large components. Full article

High-precision measurement of large gear rotation angles is a critical technology in gear meshing-based measurement systems. To address the challenge of high-precision rotation angle measurement for large gear, this paper proposes a binocular vision method. The methodology consists of the following steps: First, sub-pixel edges of calibration circles on a 2D dot-matrix calibration board are extracted using edge detection algorithms to obtain pixel coordinates of the circle centers. Second, a high-precision calibration of the measurement reference plate is achieved through a 2D four-parameter coordinate transformation algorithm. Third, binocular cameras capture images of the measurement reference plates attached to large gear before and after rotation. Coordinates of the camera’s field-of-view center in the measurement reference plate coordinate system are calculated via image processing and rotation angle algorithms, thereby determining the rotation angle of the large gear. Finally, a binocular vision rotation angle measurement system was developed, and experiments were conducted on a 600 mm-diameter gear to validate the feasibility of the proposed method. The results demonstrate a measurement accuracy of 7 arcseconds (7”) and a repeatability precision of 3 arcseconds (3”) within the 0–30° rotation range, indicating high accuracy and stability. The proposed method and system effectively meet the requirements for high-precision rotation angle measurement of large gear. Full article

Background: Eye–hand coordination (EHC) plays a critical role in daily activities and is affected by monocular vision impairment. This study evaluates existing EHC tests to detect performance decline under monocular conditions, supports the assessment and monitoring of vision rehabilitation, and quantifies the binocular advantage of each test. Methods: A total of 70 healthy sports students (aged 19–30 years) participated in four EHC tests: the Purdue Pegboard Test (PPT), Finger–Nose Test (FNT), Alternate Hand Wall Toss Test (AHWTT), and Loop-Wire Test (LWT). Each participant completed the tests under both binocular and monocular conditions in a randomized order, with assessments conducted by two independent raters. Performance differences, binocular advantage, effect sizes, and interrater reliability were analyzed. Results: Data from 66 participants were included in the final analysis. Significant performance differences between binocular and monocular conditions were observed for the LWT (p < 0.001), AHWTT (p < 0.001), and PPT (p < 0.05), with a clear binocular advantage and large effect sizes (SMD range: 0.583–1.660) for the AHWTT and LWT. Female participants performed better in fine motor tasks, while males demonstrated superior performance in gross motor tasks. Binocular performance averages aligned with published reference values. Conclusions: The findings support the inclusion of the LWT and AHWTT in clinical protocols to assess and assist individuals with monocular vision impairment, particularly following sudden uniocular vision loss. Future research should extend these findings to different age groups and clinically relevant populations. Full article

The semantic segmentation of textured 3D meshes is a critical step in constructing city-scale realistic 3D models. Compared to colored point clouds, textured 3D meshes have the advantage of high-resolution texture image patches embedded on each mesh face. However, existing studies predominantly focus on their geometric structures, with limited utilization of these high-resolution textures. Inspired by the binocular perception of humans, this paper proposes a multimodal feature fusion network based on 3D geometric structures and 2D high-resolution texture images for the semantic segmentation of textured 3D meshes. Methodologically, the 3D feature extraction branch computes the centroid coordinates and face normals of mesh faces as initial 3D features, followed by a multi-scale Transformer network to extract high-level 3D features. The 2D feature extraction branch employs orthographic views of city scenes captured from a top-down perspective and uses a U-Net to extract high-level 2D features. To align features across 2D and 3D modalities, a Bridge view-based alignment algorithm is proposed, which visualizes the 3D mesh indices to establish pixel-level associations with orthographic views, achieving the precise alignment of multimodal features. Experimental results demonstrate that the proposed method achieves competitive performance in city-scale textured 3D mesh semantic segmentation, validating the effectiveness and potential of the cross-modal fusion strategy. Full article

Spherical buoys serve as water surface markers, and their location information can help unmanned surface vessels (USVs) identify navigation channel boundaries, avoid dangerous areas, and improve navigation accuracy. However, due to the presence of disturbances such as reflections, water obstruction, and changes in illumination for spherical buoys on the water surface, using binocular vision for positioning encounters difficulties in matching. To address this, this paper proposes a monocular vision-based localization method for spherical buoys using elliptical fitting. First, the edges of the spherical buoy are extracted through image preprocessing. Then, to address the issue of pseudo-edge points introduced by reflections that reduce the accuracy of elliptical fitting, a multi-step method for eliminating pseudo-edge points is proposed. This effectively filters out pseudo-edge points and obtains accurate elliptical parameters. Finally, based on these elliptical parameters, a monocular vision ranging model is established to solve the relative position between the USV and the buoy. The USV’s position from satellite observation is then fused with the relative position calculated using the method proposed in this paper to estimate the coordinates of the buoy in the geodetic coordinate system. Simulation experiments analyzed the impact of pixel noise, camera height, focal length, and rotation angle on localization accuracy. The results show that within a range of 40 m in width and 80 m in length, the coordinates calculated by this method have an average absolute error of less than 1.2 m; field experiments on actual ships show that the average absolute error remains stable within 2.57 m. This method addresses the positioning issues caused by disturbances such as reflections, water obstruction, and changes in illumination, achieving a positioning accuracy comparable to that of general satellite positioning. Full article

In this research, I introduce a water surface elevation measurement method that combines point cloud processing techniques and stereo vision cameras. While current vision-based water level measurement techniques focus on laboratory measurements or are based on auxiliary devices such as water rulers, I investigated the feasibility of measuring elevation based on images of the water surface. This research implements a monitoring system on-site, comprising a ZED 2i binocular camera (Stereolabs, San Francisco, CA, USA). First, the uncertainty of the camera is evaluated in a real measurement scenario. Then, the water surface images captured by the binocular camera are stereo matched to obtain parallax maps. Subsequently, the results of the binocular camera calibration are utilized to obtain the 3D point cloud coordinate values of the water surface image. Finally, the horizontal plane equation is solved by the RANSAC algorithm to finalize the height of the camera on the water surface. This approach is particularly significant as it offers a non-contact, shore-based solution that eliminates the need for physical water references, thereby enhancing the adaptability and efficiency of water level monitoring in challenging environments, such as remote or inaccessible areas. Within a measured elevation of 5 m, the water level measurement error is less than 2 cm. Full article

To achieve automation at the inner corner guard installation station in a steel coil packaging production line and enable automatic docking and installation of the inner corner guard after eye position detection, this paper proposes a binocular vision method based on deep learning for eye position detection of steel coil rolls. The core of the method involves using the Mask R-CNN algorithm within a deep-learning framework to identify the target region and obtain a mask image of the steel coil end face. Subsequently, the binarized image of the steel coil end face was processed using the RGB vector space image segmentation method. The target feature pixel points were then extracted using Sobel edges, and the parameters were fitted by the least-squares method to obtain the deflection angle and the horizontal and vertical coordinates of the center point in the image coordinate system. Through the ellipse parameter extraction experiment, the maximum deviations in the pixel coordinate system for the center point in the u and v directions were 0.49 and 0.47, respectively. The maximum error in the deflection angle was 0.45°. In the steel coil roll eye position detection experiments, the maximum deviations for the pitch angle, deflection angle, and centroid coordinates were 2.17°, 2.24°, 3.53 mm, 4.05 mm, and 4.67 mm, respectively, all of which met the actual installation requirements. The proposed method demonstrates strong operability in practical applications, and the steel coil end face position solving approach significantly enhances work efficiency, reduces labor costs, and ensures adequate detection accuracy. Full article

An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent further deterioration, such as amblyopia and even permanent vision loss. To satisfy this need, this work showcases a novel strabismus screening method based on a wearable eye-tracking device combined with an artificial intelligence (AI) algorithm. To identify the minor and occasional inconsistencies in strabismus patients during the binocular coordination process, which are usually seen in early-stage patients and rarely recognized in current studies, the system captures temporally and spatially continuous high-definition infrared images of the eye during wide-angle continuous motion, and is effective in inducing intermittent strabismus. Based on the collected eye motion information, 16 features of the oculomotor process with strong physiological interpretations, which help biomedical staff understand and evaluate results generated later, are calculated through the introduction of pupil-canthus vectors. These features can be normalized, and reflect individual differences. After these features are processed by the random forest (RF) algorithm, this method experimentally yields 97.1% accuracy in strabismus detection in 70 people under diverse indoor testing conditions, validating the high accuracy and robustness of the method, and implying that the method has strong potential to support widespread and highly accurate strabismus screening. Full article

In ball game sports, binocular visual function is important for accurately perceiving the distance of various objects in visual space. However, the temporal coordination of binocular eye movements during saccades has not been investigated extensively in athletes. The purpose of the present study was to compare the characteristics found in the interocular timing differences in horizontal saccades between ball game players. The participants included 32 university baseball players and 54 university soccer players. They were asked to shift their gaze to the onset of the light-emitting diodes located at 10 deg of visual field eccentricity to the left and right and alternated every 2 s. Horizontal movements of the left and right eyes were recorded separately with the electro-oculogram. Temporal variables for each eye were calculated with digital differentiation, and timing differences between the left and right eyes were compared between participant groups. The overall results showed significant interocular differences between left and right eye movements for the temporal variables of binocular saccades. The comparison between the participant groups revealed that baseball players had smaller interocular timing differences between the left and right eyes than soccer players in the onset time, time to peak velocity, duration, and peak velocity. These results suggest that baseball players have a higher degree of temporal coordination in binocular eye movements, particularly during the initial phase of horizontal saccades, compared to soccer players. This enhanced coordination might be attributable to the sport-specific visual demands of baseball, where players require precise stereoscopic vision to track a small high-speed ball within their visual space. Full article

Currently, research on peg-in-hole (PiH) compliant assembly is predominantly limited to circular pegs and holes, with insufficient exploration of various complex-shaped PiH tasks. Furthermore, the degree of freedom for rotation about the axis of the circular peg cannot be constrained after assembly, and few studies have covered the complete process from autonomous hole-searching to insertion. Based on the above problems, a novel cross-shaped peg and hole design has been devised. The center coordinates of the cross-hole are obtained during the hole-searching process using the three-dimensional reconstruction theory of a binocular stereo vision camera. During the insertion process, 26 contact states of the cross-peg and the cross-hole were classified, and the mapping relationship between the force-moment sensor and relative errors was established based on a backpropagation (BP) neural network, thus completing the task of autonomous PiH assembly. This system avoids hand-guiding, completely realizes the autonomous assembly task from hole-searching to insertion, and can be replaced by other structures of pegs and holes for repeated assembly after obtaining the accurate relative pose between two assembly platforms, which provides a brand-new and unified solution for complex-shaped PiH assembly. Full article

The advancement of unloading technology in combine harvesting is crucial for the intelligent development of agricultural machinery. Accurately measuring material pile height in transport vehicles is essential, as uneven accumulation can lead to spillage and voids, reducing loading efficiency. Relying solely on manual observation for measuring stack height can decrease harvesting efficiency and pose safety risks due to driver distraction. This research applies binocular vision to agricultural harvesting, proposing a novel method that uses a stereo matching algorithm to measure material pile height during harvesting. By comparing distance measurements taken in both empty and loaded states, the method determines stack height. A linear regression model processes the stack height data, enhancing measurement accuracy. A binocular vision system was established, applying Zhang’s calibration method on the MATLAB (R2019a) platform to correct camera parameters, achieving a calibration error of 0.15 pixels. The study implemented block matching (BM) and semi-global block matching (SGBM) algorithms using the OpenCV (4.8.1) library on the PyCharm (2020.3.5) platform for stereo matching, generating disparity, and pseudo-color maps. Three-dimensional coordinates of key points on the piled material were calculated to measure distances from the vehicle container bottom and material surface to the binocular camera, allowing for the calculation of material pile height. Furthermore, a linear regression model was applied to correct the data, enhancing the accuracy of the measured pile height. The results indicate that by employing binocular stereo vision and stereo matching algorithms, followed by linear regression, this method can accurately calculate material pile height. The average relative error for the BM algorithm was 3.70%, and for the SGBM algorithm, it was 3.35%, both within the acceptable precision range. While the SGBM algorithm was, on average, 46 ms slower than the BM algorithm, both maintained errors under 7% and computation times under 100 ms, meeting the real-time measurement requirements for combine harvesting. In practical operations, this method can effectively measure material pile height in transport vehicles. The choice of matching algorithm should consider container size, material properties, and the balance between measurement time, accuracy, and disparity map completeness. This approach aids in manual adjustment of machinery posture and provides data support for future autonomous master-slave collaborative operations in combine harvesting. Full article

Vessel-mounted cranes operate in complex marine environments, where precise measurement of cargo positions and attitudes is a key technological challenge to ensure operational stability and safety. This study introduces an integrated measurement system that combines vision and inertial sensing technologies, utilizing a stereo camera and two inertial measurement units (IMUs) to capture cargo motion in five degrees of freedom (DOF). By merging data from the stereo camera and IMUs, the system accurately determines the cargo’s position and attitude relative to the camera. The specific methodology is introduced as follows: First, the YOLO model is adopted to identify targets in the image and generate bounding boxes. Then, using the principle of binocular disparity, the depth within the bounding box is calculated to determine the target’s three-dimensional position in the camera coordinate system. Simultaneously, the IMU measures the attitude of the cargo, and a Kalman filter is applied to fuse the data from the two sensors. Experimental results indicate that the system’s measurement errors in the x, y, and z directions are less than 2.58%, 3.35%, and 3.37%, respectively, while errors in the roll and pitch directions are 3.87% and 5.02%. These results demonstrate that the designed measurement system effectively provides the necessary motion information in 5-DOF for vessel-mounted crane control, offering new approaches for pose detection of marine cranes and cargoes. Full article

The rotational speed vectors of the bearing balls affect their service life and running performance. Observing the actual rotational speed of the ball is a prerequisite for revealing its true motion law and conducting sliding behavior simulation analysis. To address the need for accuracy and real-time measurement of spin angular velocity, which is also under high-frequency and high-speed ball motion conditions, a new measurement method of ball rotation vectors based on a binocular vision system is proposed. Firstly, marker points are laid on the balls, and their three-dimensional (3D) coordinates in the camera coordinate system are calculated in real time using the triangulation principle. Secondly, based on the 3D coordinates before and after the movement of the marker point and the trajectory of the ball, the mathematical model of the spin motion of the ball was established. Finally, based on the ball spin motion model, the three-dimensional vision measurement technology was first applied to the measurement of the bearing ball rotation vector through formula derivation, achieving the analysis of bearing ball rolling and sliding characteristics. Experimental results demonstrate that the visual measurement system with the frame rate of 100 FPS (frames per second) yields a measurement error within ±0.2% over a speed range from 5 to 50 RPM (revolutions per minute), and the maximum measurement errors of spin angular velocity and linear velocity are 0.25 °/s and 0.028 mm/s, respectively. The experimental results show that this method has good accuracy and stability in measuring the rotation vector of the ball, providing a reference for bearing balls’ rotational speed monitoring and the analysis of the sliding behavior of bearing balls. Full article