Search Results (608)

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

High-precision positioning is the foundation for the functionality of various intelligent agents. In complex environments, such as urban canyons, relative pose estimation using cameras is a crucial step in high-precision positioning. To take advantage of the ability of an inertial measurement unit (IMU) to provide relatively accurate gravity prior information over a short period, we propose a minimal solution method for the relative pose estimation of a stereo camera system assisted by the IMU. We rigidly connect the IMU to the camera system and use it to obtain the rotation matrices in the roll and pitch directions for the entire system, thereby reducing the minimum number of corresponding points required for relative pose estimation. In contrast to classic pose-estimation algorithms, our method can also calculate the camera focal length, which greatly expands its applicability. We constructed a simulated dataset and used it to compare and analyze the numerical stability of the proposed method and the impact of different levels of noise on algorithm performance. We also collected real-scene data using a drone and validated the proposed algorithm. The results on real data reveal that our method exhibits smaller errors in calculating the relative pose of the camera system compared with two classic reference algorithms. It achieves higher precision and stability and can provide a comparatively accurate camera focal length. Full article

This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments. Full article

Recent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation for harvesting ripe fruits in greenhouse environments, achieving servo control of robotic arms with flexible end-effectors. The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. Second, a 3D camera acquires the target fruit’s spatial position and orientation data in real time, and these data are stored in the collaborative robot’s microcontroller. Finally, employing binocular calibration and triangulation, the SLAM navigation module guides the robotic arm to the designated picking location via unobstructed target positioning. Comprehensive comparative experiments between the improved YOLO v12n model and earlier versions were conducted to validate its performance. The results demonstrate that the optimized model surpasses traditional recognition and harvesting methods, offering superior target fruit identification response (minimum 30.9ms) and significantly higher accuracy (91.14%). Full article

In this work, we propose a geolocation framework for distant ground targets integrating laser rangefinder sensors with multimodal visual servo control. By simulating binocular visual servo measurements through monocular visual servo tracking at fixed time intervals, our approach requires only single-session sensor attitude correction calibration to accurately geolocalize multiple targets during a single flight, which significantly enhances operational efficiency in multi-target geolocation scenarios. We design a step-convergent target geolocation optimization algorithm. By adjusting the step size and the scale factor of the cost function, we achieve fast accuracy convergence for different UAV reconnaissance modes, while maintaining the geolocation accuracy without divergence even when the laser ranging sensor is turned off for a short period. The experimental results show that through the UAV’s continuous reconnaissance measurements, the geolocalization error of remote ground targets based on our algorithm is less than 7 m for 3000 m, and less than 3.5 m for 1500 m. We have realized the fast and high-precision geolocalization of remote targets on the ground under the high-altitude reconnaissance of UAVs. Full article

The research aims to compare the dynamic visual acuity (DVA) in myopic adults wearing spectacles prescribed to 0.05D and 0.25D steps. This double-blind, randomized, self-control study included 40 myopic participants aged 18–40. The participants were randomly assigned to receive spectacles with one 0.05D step lens and the contralateral lens of 0.25D step. The monocular horizontal and vertical motion DVA at 20 and 40 degrees per second (dps) was measured. The DVA was compared between eyes with 0.25D and 0.05D step lenses and further analyzed by eye dominance and test sequence. The result demonstrated no significant difference in DVA between two eyes with 0.25D or 0.05D step lenses at 20 and 40 dps horizontal and vertical motion test (p > 0.05, respectively). When the eye with a 0.25D step lens was the dominant eye (p = 0.004) or measured secondly (p = 0.002), it outperformed the contralateral eye with a 0.05D step lens in the 40 dps horizontal motion test. In conclusion, the horizontal and vertical motion DVA of the eye with 0.05D step lens spectacles was comparable to that of contralateral eyes corrected with 0.25D step lens. Full article

Background/Objectives: A direct link between sensory processing disorder (SPD) and strabismus has not been systematically investigated, though prior studies suggest sensory modulation may influence visual behaviors. Traditional approaches view strabismus through a binary lens—either normal or pathological motor deviation. This report presents a proof-of-concept case suggesting strabismus may represent a neurobehavioral manifestation of sensory processing imbalance, rooted within the broader framework of SPD. Methods: We report a pediatric case marked by episodic monocular eye closure triggered by environmental stimuli, without identifiable ophthalmologic or neurologic pathology. The child’s symptoms were most consistent with sensory over-responsivity (SOR), a subtype of SPD, manifesting as stimulus-bound monocular eye closure and secondary self-regulatory behaviors. Results: We propose the Fusion Dysregulation Hypothesis, suggesting that exotropia and esotropia represent opposing outcomes along a continuum of sensory connectivity: exotropia arising from neural underwiring (hyporesponsivity and fusion instability), and esotropia from overwiring (hyperresponsivity and excessive fusion drive). Our case, marked by sensory hyperresponsivity, showed frequent monocular eye closure that briefly disrupted but did not impair fusion. This suggests an “overwired” binocular system maintaining single vision despite sensory triggers. In early-onset esotropia, such overconnectivity may become maladaptive, leading to sustained convergence. Conversely, autism spectrum disorder, typically associated with hypoconnectivity, may predispose to exotropia through reduced fusion maintenance. Conclusions: These findings highlight the need for interdisciplinary evaluation. We advocate for structured sensory profiling in children presenting with strabismus and, conversely, for ophthalmologic assessment in those diagnosed with SPD. While our findings remain preliminary, they support a bidirectional screening approach and suggest that sensory modulation may play a previously under-recognized role in the spectrum of pediatric strabismus presentations. 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

With the advancement of agricultural automation technologies, apple-harvesting robots have gradually become a focus of research. As their “perceptual core,” machine vision systems directly determine picking success rates and operational efficiency. However, existing vision systems still exhibit significant shortcomings in target detection and positioning accuracy in complex orchard environments (e.g., uneven illumination, foliage occlusion, and fruit overlap), which hinders practical applications. This study proposes a visual system for apple-harvesting robots based on improved Mask R-CNN and binocular vision to achieve more precise fruit positioning. The binocular camera (ZED2i) carried by the robot acquires dual-channel apple images. An improved Mask R-CNN is employed to implement instance segmentation of apple targets in binocular images, followed by a template-matching algorithm with parallel epipolar constraints for stereo matching. Four pairs of feature points from corresponding apples in binocular images are selected to calculate disparity and depth. Experimental results demonstrate average coefficients of variation and positioning accuracy of 5.09% and 99.61%, respectively, in binocular positioning. During harvesting operations with a self-designed apple-picking robot, the single-image processing time was 0.36 s, the average single harvesting cycle duration reached 7.7 s, and the comprehensive harvesting success rate achieved 94.3%. This work presents a novel high-precision visual positioning method for apple-harvesting robots. Full article

As the prevalence of related diseases continues to rise, a corresponding increase in the demand for internal limiting membrane (ILM) peeling surgery has been observed. However, significant challenges are encountered in ILM peeling surgery, including limited force feedback, inadequate depth perception, and surgeon hand tremors. Research on fully autonomous ILM peeling surgical robots has been conducted to address the imbalance between medical resource availability and patient demand while enhancing surgical safety. An automatic control framework for break initiation in ILM peeling is proposed in this study, which integrates model-predictive control with fractional-order admittance control. Additionally, a multi-vision task surgical scene perception method is introduced based on target detection, key point recognition, and sparse binocular matching. A surgical trajectory planning strategy for break initiation in ILM peeling aligned with operative specifications is proposed. Finally, validation experiments for automatic break initiation in ILM peeling were performed using eye phantoms. The results indicated that the positional error of the micro-forceps tip remained within 40 μm. At the same time, the contact force overshoot was limited to under 6%, thereby ensuring both the effectiveness and safety of break initiation during ILM peeling. Full article

Purpose: A novel enhanced monofocal intraocular lens (IOL) has been developed to improve functional intermediate vision, maintaining a distance vision comparable to a standard monofocal lens and avoiding the drawbacks of multifocal IOLs. The aim of this study is to perform optical bench analysis and to evaluate refractive and visual outcomes and patient satisfaction. Methods: This prospective comparative single-center study was conducted in Careggi Hospital, University of Florence (Italy). We included 100 eyes from 50 patients who underwent bilateral cataract surgery. One group received the standard monofocal Tecnis GCB00 IOL, while the other group received the novel enhanced monofocal Evolux IOL. We evaluated binocular visual and refractive outcomes at 6 months after surgery. Binocular defocus curves and contrast sensitivity (CS) were also assessed. Optical quality was also analyzed in terms of higher-order aberrations (HOAs), modulation transfer function (MTF), objective scatter index (OSI), Strehl ratio, effective lens position (ELP), and halo analysis. A Patient-Reported Spectacle Independence Questionnaire (PRSIQ) was performed to assess spectacle independence outcomes. Finally, we analyzed the optical bench of both lenses. Results: All eyes implanted with Evolux achieved excellent distance vision, comparable to that achieved with GCB00. Evolux showed better intermediate and near vision, without any loss of visual quality, contrast sensitivity, or the presence of halos and photic phenomena. The optical bench analysis confirmed the different optical properties of the two lenses and supported the behavior obtained with the clinical defocus curve. Conclusions: These preliminary results show good refractive accuracy and visual outcomes for the enhanced monofocal IOL Evolux after cataract surgery. Further studies are needed to confirm our findings in terms of the number of patients and the period of follow-up. Full article

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element. Full article

In the mix-and-match approach and in binocular intraocular lens (IOL) systems, two different IOL models are implanted in each eye to achieve the desired binocular outcome. In the mix-and-match approach, the surgeon selects the IOL models to be combined according to the clinical situation, the patient’s needs, and personal preference. Combinations described in the literature include, among others, two bifocal IOLs, an extended-depth-of-focus (EDoF) IOL with a bifocal lens, a trifocal lens with an EDoF IOL or an enhanced monofocal IOL, and two EDoF models utilizing different optical principles. The outcomes depend on the selected combination of IOL models. Binocular IOL systems consist of a fixed combination of two lenses, developed to be complementary in binocular vision. Initial data indicate that they can achieve a depth of focus similar to that with a bilateral implantation of a trifocal IOL. However, comparative studies are needed to evaluate if the postoperative binocular outcome differs from that achieved with the conventional approach. The mix-and-match implantation and binocular IOL systems expand the options available to tailor the IOL selection to the patient’s needs. Full article

In fields such as military reconnaissance, forest fire prevention, and autonomous driving at night, there is an urgent need for high-precision three-dimensional reconstruction in low-light or night environments. The acquisition of remote sensing data by RGB cameras relies on external light, resulting in a significant decline in image quality and making it difficult to meet the task requirements. The method based on lidar has poor imaging effects in rainy and foggy weather, close-range scenes, and scenarios requiring thermal imaging data. In contrast, infrared cameras can effectively overcome this challenge because their imaging mechanisms are different from those of RGB cameras and lidar. However, the research on three-dimensional scene reconstruction of infrared images is relatively immature, especially in the field of infrared binocular stereo matching. There are two main challenges given this situation: first, there is a lack of a dataset specifically for infrared binocular stereo matching; second, the lack of texture information in infrared images causes a limit in the extension of the RGB method to the infrared reconstruction problem. To solve these problems, this study begins with the construction of an infrared binocular stereo matching dataset and then proposes an innovative perspective projection positional encoding-based transformer method to complete the infrared binocular stereo matching task. In this paper, a stereo matching network combined with transformer and cost volume is constructed. The existing work in the positional encoding of the transformer usually uses a parallel projection model to simplify the calculation. Our method is based on the actual perspective projection model so that each pixel is associated with a different projection ray. It effectively solves the problem of feature extraction and matching caused by insufficient texture information in infrared images and significantly improves matching accuracy. We conducted experiments based on the infrared binocular stereo matching dataset proposed in this paper. Experiments demonstrated the effectiveness of the proposed method. Full article

As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. This study presents a YOLOv8-based binocular vision model for real-time recognition of blasting fragmentation. The model is trained on a dataset comprising 1536 samples, which were annotated using an automatic labeling algorithm and expanded to 7680 samples through data augmentation techniques. The YOLOv8 instance segmentation model is employed to detect and classify rock fragments. By integrating binocular vision-based automatic image capture with Welzl’s algorithm, the actual particle size of each rock fragment is calculated. Furthermore, region of interest (ROI) extraction and shadow-based data enhancement techniques are incorporated to focus the model on the blasting fragmentation area and reduce environmental interference. Finally, software and a system were independently developed based on this integrated model and successfully deployed at engineering sites. The dynamic recognition Mean Average Precision of this integrated model is 0.84, providing a valuable reference for evaluating blasting effects and improving work efficiency. Full article