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In this paper, a high dynamic range (HDR) imaging method based on the stereo vision system is presented. The proposed method uses differently exposed low dynamic range (LDR) images captured from a stereo camera. The stereo LDR images are first converted to initial stereo HDR images using the inverse camera response function estimated from the LDR images. However, due to the limited dynamic range of the stereo LDR camera, the radiance values in under/over-exposed regions of the initial main-view (MV) HDR image can be lost. To restore these radiance values, the proposed stereo matching and hole-filling algorithms are applied to the stereo HDR images. Specifically, the auxiliary-view (AV) HDR image is warped by using the estimated disparity between initial the stereo HDR images and then effective hole-filling is applied to the warped AV HDR image. To reconstruct the final MV HDR, the warped and hole-filled AV HDR image is fused with the initial MV HDR image using the weight map. The experimental results demonstrate objectively and subjectively that the proposed stereo HDR imaging method provides better performance compared to the conventional method.

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

Calibration of the stereo vision systems is a crucial step for precise 3D measurements. Restricted by the outdoors’ large field of view (FOV), the conventional method based on precise calibration boards is not suitable since the calibration process is time consuming and the calibration accuracy is not guaranteed. In this paper, we propose a calibration method for estimating the extrinsic parameters of the stereo vision system aided by an inclinometer and a range sensor. Through the parameters given by the sensors, the initial rotation angle of the extrinsic parameters and the translation vector are pre-established by solving a set of linear equations. The metric scale of the translation vector is determined by the baseline length provided by the range sensor or GNSS signals. Finally, the optimal extrinsic parameters of the stereo vision systems are obtained by nonlinear optimization of inverse depth parameterization. The most significant advantage of this method is that it enhances the capability of the stereo vision measurement in the outdoor environment, and can achieve fast and accurate calibration results. Both simulation and outdoor experiments have verified the feasibility and correctness of this method, and the relative error in the outdoor large FOV was less than 0.3%. It shows that this calibration method is a feasible solution for outdoor measurements with a large FOV and long working distance.

A multi-unmanned aerial vehicle (UAV)-based stereo vision system is proposed to assist global path planning of an unmanned ground vehicle (UGV) even in GPS-denied environments. The proposed system can optimally generate the depth map of ground objects and robustly detect obstacles. The proposed multi-UAV-based system with a movable baseline overcomes the limitations of a single-UAV-based stereo vision system with a fixed baseline. Thus, the performance of the proposed system does not degrade significantly based on the altitude of UAVs. The relative position and altitude estimation, multi-agent formation control and image processing techniques are considered to implement a prototype system. The experimental results demonstrate the performance of the implemented system for various baseline conditions between UAVs.

Automated apple harvesting is hindered by clustered fruits, varying illumination, and inconsistent depth perception in complex orchard environments. While deep learning models such as Faster R-CNN and YOLO provide accurate 2D detection, they require large annotated datasets and high computational resources, and often lack the precise 3D localisation required for robotic picking. This study proposes an enhanced K-Means clustering segmentation algorithm integrated with a stereo-vision system for accurate 3D apple localisation. Multi-feature fusion combining colour, morphology, and texture descriptors was applied to improve segmentation robustness. A block-matching stereo model was used to compute disparity and derive 3D coordinates. The method was evaluated against Faster R-CNN, YOLOv7, Mask R-CNN, SSD, DBSCAN, MISA, and HCA using metrics including Recognition Accuracy (RA), mean Average Precision (mAP), Mean Coordinate Deviation (MCD), Correct Recognition Rate (CRR), Frames Per Second (FPS), and depth-localisation error. The proposed method achieved >91% detection accuracy and <1% localisation error across challenging orchard conditions. Compared with Faster R-CNN, it maintained higher RA and lower MCD under high fruit overlap and variable lighting. Depth estimation achieved errors between 0.4%-0.97% at 800-1100 mm distances, confirming high spatial accuracy. The proposed model exceeded YOLOv7, SSD, FCN, and Mask R-CNN in F1-score, mAP, and FPS during complex lighting, occlusion, wind disturbance, and dense fruit distributions. The clustering-based stereo-vision framework provides stable 3D localisation and robust segmentation without large training datasets or high-performance hardware. Its low computational demand and strong performance under diverse orchard conditions make it suitable for real-time robotic harvesting. Future work will focus on large-scale orchard deployment, parallel optimisation, and adaptation to additional fruit types.

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding$200 million annually, rising to $ 1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

A tomato harvesting robot was developed in this study, which consisted of a four-wheel independent steering system, a 5-DOF harvesting system, a navigation system, and a binocular stereo vision system. The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry. The proportional-integral-derivative (PID) algorithm was used in the laser navigation control system. The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes, and obstacle avoidance strategies were proposed based on the C-space method. The maximum average absolute error between the set angle and the actual angle was about 0.14°, and the maximum standard deviation was about 0.04°. The laser navigation system was able to rapidly and accurately track the path, with the deviation being less than 8 cm. The load bearing capacity of the mechanical arm was about 1.5 kg. The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%. When the distance was less than 600 mm, the positioning error was less than 10 mm. The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato, with a success rate of about 86%. This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.

The inspection-class Remotely Operated Vehicles (ROVs) are crucial in underwater inspections. Their prime function is to allow the replacing of humans during risky subaquatic operations. These vehicles gather videos from underwater scenes that are sent online to a human operator who provides control. Furthermore, these videos are used for analysis. This demands an RGB camera operating at a close distance to the observed objects. Thus, to obtain a detailed depiction, the vehicle should move with a constant speed and a measured distance from the bottom. As very few inspection-class ROVs possess navigation systems that facilitate these requirements, this study had the objective of designing a vision-based control method to compensate for this limitation. To this end, a stereo vision system and image-feature matching and tracking techniques were employed. As these tasks are challenging in the underwater environment, we carried out analyses aimed at finding fast and reliable image-processing techniques. The analyses, through a sequence of experiments designed to test effectiveness, were carried out in a swimming pool using a VideoRay Pro 4 vehicle. The results indicate that the method under consideration enables automatic control of the vehicle, given that the image features are present in stereo-pair images as well as in consecutive frames captured by the left camera.

Aiming at the problem of limited imaging quality of monomodal optical cameras in low-light environments, this paper constructs a thermal infrared-RGB binocular stereo vision system and proposes a joint calibration framework for infrared and RGB cameras to provide a high-precision geometric alignment basis for multimodal image fusion. First, a high-precision geometric calibration method is used to eliminate the internal distortion of the infrared camera and establish the mapping relationship between its pixel coordinate system and physical space. Second, a cross-modal extrinsic calibration strategy based on common view targets is designed. A specially designed heated and temperature-controlled chessboard calibration board for thermal infrared is used to enhance the feature contrast in the infrared image through temperature control. Combined with a cross-modal feature matching algorithm, the spatial pose transformation matrix between the infrared and RGB cameras is accurately solved to align multimodal images. Experimental results show that the proposed thermal infrared–RGB binocular calibration method can significantly improve calibration accuracy and robustness, providing effective technical support for visual perception and target recognition in low-light environments.

Developing new methods to detect biomass information on freshwater fish in farm conditions enables the creation of decision bases for precision feeding. In this study, an approach based on Keypoints R-CNN is presented to identify species and measure length automatically using an underwater stereo vision system. To enhance the model’s robustness, stochastic enhancement is performed on image datasets. For further promotion of the features extraction capability of the backbone network, an attention module is integrated into the ResNeXt50 network. Concurrently, the feature pyramid network (FPN) is replaced by an improved path aggregation network (I-PANet) to achieve a greater fusion of effective feature maps. Compared to the original model, the mAP of the improved one in object and key point detection tasks increases by 4.55% and 2.38%, respectively, with a small increase in the number of model parameters. In addition, a new algorithm is introduced for matching the detection results of neural networks. On the foundation of the above contents, coordinates of head and tail points in stereo images as well as fish species can be obtained rapidly and accurately. A 3D reconstruction of the fish head and tail points is performed utilizing the calibration parameters and projection matrix of the stereo camera. The estimated length of the fish is acquired by calculating the Euclidean distance between two points. Finally, the precision of the proposed approach proved to be acceptable for five kinds of common freshwater fish. The accuracy of species identification exceeds 94%, and the relative errors of length measurement are less than 10%. In summary, this method can be utilized to help aquaculture farmers efficiently collect real-time information about fish length.