Search Results (20)

The size and mass of fish are crucial parameters in aquaculture management. However, existing research primarily focuses on conducting fish size and mass estimation under ideal conditions, which limits its application in actual aquaculture scenarios with complex water quality and fluctuating lighting. A non-contact size and mass measurement framework is proposed for complex underwater environments, which integrates the improved FishKP-YOLOv11 module based on YOLOv11, stereo vision technology, and a Random Forest model. This framework fuses the detected 2D key points with binocular stereo technology to reconstruct the 3D key point coordinates. Fish size is computed based on these 3D key points, and a Random Forest model establishes a mapping relationship between size and mass. For validating the performance of the framework, a self-constructed grass carp dataset for key point detection is established. The experimental results indicate that the mean average precision (mAP) of FishKP-YOLOv11 surpasses that of diverse versions of YOLOv5–YOLOv12. The mean absolute errors (MAEs) for length and width estimations are 0.35 cm and 0.10 cm, respectively. The MAE for mass estimations is 2.7 g. Therefore, the proposed framework is well suited for application in actual breeding environments. Full article

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries. Full article

With the gradual application of 3D reconstruction technology in underwater scenes, the design of vision-based reconstruction models has become an important research direction for human ocean exploration and development. The underwater laser triangulation method is the most commonly used approach, yet it misses details during the reconstruction of sparse point clouds, which do not meet the requirements of practical applications. On the other hand, existing underwater photometric stereo methods can accurately reconstruct local details of target objects, but they require relative stillness to be maintained between the camera and the target, which is practically difficult to achieve in underwater imaging environments. In this paper, we propose an underwater target reconstruction algorithm that combines laser triangulation and multispectral photometric stereo (MPS) to address the aforementioned practical problems in underwater 3D reconstruction.This algorithm can obtain more comprehensive 3D surface data of underwater objects through mobile measurement. At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. The experimental results show that our method achieves higher-precision and higher-density 3D reconstruction than current state-of-the-art methods. Full article

When light passes through air–glass and glass–water interfaces, refraction occurs, which affects the accuracy of stereo vision three-dimensional measurements of underwater targets. To eliminate the impact of refraction, we developed a refractive stereo vision measurement model based on light propagation paths, utilizing the normalized coordinate of the underwater target. This model is rigorous in theory, and easy to understand and apply. Additionally, we established an underwater simulation imaging model based on the principle that light travels the shortest time between two points. Simulation experiments conducted using this imaging model verified the performance of the underwater stereo vision measurement model. The results demonstrate that the accuracy achieved by the new measurement model is comparable to that of the stereo vision measurement model in the air and significantly higher than that of the existing refractive measurement model. This is because the light rays from the camera’s optical center to the refraction point at the air–glass interface do not always intersect. The experiments also indicate that the deviation in the refractive index of water lead to corresponding systematic errors in the measurement results. Therefore, in real underwater measurements, it is crucial to carefully calibrate the refractive index of water and maintain the validity of the calibration results. Full article

There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging models based on the actual geometry of light refraction to compensate for the effects of refraction on cross-media imaging. However, the calibration of refraction parameters inevitably contains errors, leading to deviations in these parameters. To analyze the impact of refraction-parameter deviations on measurements in underwater structure visual navigation, this paper develops a dual-media stereo-vision measurement simulation model and conducts comprehensive simulation experiments. The results indicate that to achieve high-precision underwater-measurement outcomes, the calibration method for refraction parameters, the distribution of the targets in the field of view, and the distance of the target from the camera must all be meticulously designed. These findings provide guidance for the construction of underwater stereo-vision measurement systems, the calibration of refraction parameters, underwater experiments, and practical applications. Full article

Vision-based underwater exploration is crucial for marine research. However, the degradation of underwater images due to light attenuation and scattering poses a significant challenge. This results in the poor visual quality of underwater images and impedes the development of vision-based underwater exploration systems. Recent popular learning-based Underwater Image Enhancement (UIE) methods address this challenge by training enhancement networks with annotated image pairs, where the label image is manually selected from the reference images of existing UIE methods since the groundtruth of underwater images do not exist. Nevertheless, these methods encounter uncertainty issues stemming from ambiguous multiple-candidate references. Moreover, they often suffer from local perception and color perception limitations, which hinder the effective mitigation of wide-range underwater degradation. This paper proposes a novel NUAM-Net (Novel Underwater Image Enhancement Attention Mechanism Network) that addresses these limitations. NUAM-Net leverages a probabilistic training framework, measuring enhancement uncertainty to learn the UIE mapping from a set of ambiguous reference images. By extracting features from both the RGB and LAB color spaces, our method fully exploits the fine-grained color degradation clues of underwater images. Additionally, we enhance underwater feature extraction by incorporating a novel Adaptive Underwater Image Enhancement Module (AUEM) that incorporates both local and long-range receptive fields. Experimental results on the well-known UIEBD benchmark demonstrate that our method significantly outperforms popular UIE methods in terms of PSNR while maintaining a favorable Mean Opinion Score. The ablation study also validates the effectiveness of our proposed method. Full article

To improve detection efficiency and reduce cost consumption in fishery surveys, target detection methods based on computer vision have become a new method for fishery resource surveys. However, the specialty and complexity of underwater photography result in low detection accuracy, limiting its use in fishery resource surveys. To solve these problems, this study proposed an accurate method named BSSFISH-YOLOv8 for fish detection in natural underwater environments. First, replacing the original convolutional module with the SPD-Conv module allows the model to lose less fine-grained information. Next, the backbone network is supplemented with a dynamic sparse attention technique, BiFormer, which enhances the model’s attention to crucial information in the input features while also optimizing detection efficiency. Finally, adding a 160 × 160 small target detection layer (STDL) improves sensitivity for smaller targets. The model scored 88.3% and 58.3% in the two indicators of mAP@50 and mAP@50:95, respectively, which is 2.0% and 3.3% higher than the YOLOv8n model. The results of this research can be applied to fishery resource surveys, reducing measurement costs, improving detection efficiency, and bringing environmental and economic benefits. Full article

The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation method, which can accurately estimate the mass of free-swimming tilapia under non-contact conditions. First, image enhancement is performed on the original image, and the depth image is obtained by correcting and stereo matching the enhanced image using binocular stereo vision technology. And the fish body is segmented by an SAM model. Then, the segmented fish body is labeled with key points, thus realizing the 3D reconstruction of tilapia. Five mass estimation models are established based on the relationship between the body length and the mass of tilapia, so as to realize the mass estimation of tilapia. The results showed that the average relative errors of the method models were 5.34%~7.25%. The coefficient of determination of the final tilapia mass estimation with manual measurement was 0.99, and the average relative error was 5.90%. The improvement over existing deep learning methods is about 1.54%. This study will provide key technical support for the non-destructive measurement of tilapia, which is of great significance to the information management of aquaculture, the assessment of fish growth condition, and baiting control. Full article

In the realm of ocean engineering and maintenance of subsea structures, accurate underwater distance quantification plays a crucial role. However, the precision of such measurements is often compromised in underwater environments due to backward scattering and feature degradation, adversely affecting the accuracy of visual techniques. Addressing this challenge, our study introduces a groundbreaking method for underwater object measurement, innovatively combining image sonar with stereo vision. This approach aims to supplement the gaps in underwater visual feature detection with sonar data while leveraging the distance information from sonar for enhanced visual matching. Our methodology seamlessly integrates sonar data into the Semi-Global Block Matching (SGBM) algorithm used in stereo vision. This integration involves introducing a novel sonar-based cost term and refining the cost aggregation process, thereby both elevating the precision in depth estimations and enriching the texture details within the depth maps. This represents a substantial enhancement over existing methodologies, particularly in the texture augmentation of depth maps tailored for subaquatic environments. Through extensive comparative analyses, our approach demonstrates a substantial reduction in measurement errors by 1.6%, showing significant promise in challenging underwater scenarios. The adaptability and accuracy of our algorithm in generating detailed depth maps make it particularly relevant for underwater infrastructure maintenance, exploration, and inspection. Full article

In this paper, we introduce a method for automated seaweed growth monitoring by combining a low-cost RGB and stereo vision camera. While current vision-based seaweed growth monitoring techniques focus on laboratory measurements or above-ground seaweed, we investigate the feasibility of the underwater imaging of a vertical seaweed farm. We use deep learning-based image segmentation (DeeplabV3+) to determine the size of the seaweed in pixels from recorded RGB images. We convert this pixel size to meters squared by using the distance information from the stereo camera. We demonstrate the performance of our monitoring system using measurements in a seaweed farm in the River Scheldt estuary (in The Netherlands). Notwithstanding the poor visibility of the seaweed in the images, we are able to segment the seaweed with an intersection of the union (IoU) of 0.9, and we reach a repeatability of 6% and a precision of the seaweed size of 18%. Full article

Professional swimming coaches make use of videos to evaluate their athletes’ performances. Specifically, the videos are manually analyzed in order to observe the movements of all parts of the swimmer’s body during the exercise and to give indications for improving swimming technique. This operation is time-consuming, laborious and error prone. In recent years, alternative technologies have been introduced in the literature, but they still have severe limitations that make their correct and effective use impossible. In fact, the currently available techniques based on image analysis only apply to certain swimming styles; moreover, they are strongly influenced by disturbing elements (i.e., the presence of bubbles, splashes and reflections), resulting in poor measurement accuracy. The use of wearable sensors (accelerometers or photoplethysmographic sensors) or optical markers, although they can guarantee high reliability and accuracy, disturb the performance of the athletes, who tend to dislike these solutions. In this work we introduce swimmerNET, a new marker-less 2D swimmer pose estimation approach based on the combined use of computer vision algorithms and fully convolutional neural networks. By using a single 8 Mpixel wide-angle camera, the proposed system is able to estimate the pose of a swimmer during exercise while guaranteeing adequate measurement accuracy. The method has been successfully tested on several athletes (i.e., different physical characteristics and different swimming technique), obtaining an average error and a standard deviation (worst case scenario for the dataset analyzed) of approximately 1 mm and 10 mm, respectively. Full article

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. Full article

As we know, for autonomous robots working in a complex underwater region, obstacle avoidance design will play an important role in underwater tasks. In this paper, a binocular-vision-based underwater obstacle avoidance mechanism is discussed and verified with our self-made Underwater Quadrocopter Vehicle. The proposed Underwater Quadrocopter Vehicle (UQV for short), like a quadrocopter drone working underwater, is a new kind of Autonomous Underwater Vehicle (AUV), which is equipped with four propellers along the vertical direction of the robotic body to adjust its body posture and two propellers arranged at the sides of the robotic body to provide propulsive and turning force. Moreover, an underwater binocular-vision-based obstacle positioning method is studied to measure an underwater spherical obstacle’s radius and its distance from the UQV. Due to its perfect ability of full-freedom underwater actions, the proposed UQV has obvious advantages such as a zero turning radius compared with existing torpedo-shaped AUVs. Therefore, one semicircle-curve-based obstacle avoidance path is planned on the basis of an obstacle’s coordinates. Practical pool experiments show that the proposed binocular vision can locate an underwater obstacle accurately, and the designed UQV has the ability to effectively avoid multiple obstacles along the predefined trajectory. Full article

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. Full article

The use of an autonomous underwater vehicle (AUV) to inspect underwater industrial infrastructure requires the precise, coordinated movement of the AUV relative to subsea objects. One significant underwater infrastructure system is the subsea production system (SPS), which includes wells for oil and gas production, located on the seabed. The present paper suggests a method for the accurate navigation of AUVs in a distributed SPS to coordinate space using video information. This method is based on the object recognition and computation of the AUV coordinate references to SPS objects. Stable high accuracy during the continuous movement of the AUV in SPS space is realized through the regular updating of the coordinate references to SPS objects. Stereo images, a predefined geometric SPS model, and measurements of the absolute coordinates of a limited number of feature points of objects are used as initial data. The matrix of AUV coordinate references to the SPS object coordinate system is computed using 3D object points matched with the model. The effectiveness of the proposed method is estimated based on the results of computational experiments with virtual scenes generated in the simulator for AUV, and with real data obtained by the Karmin2 stereo camera (Nerian Vision, Stuttgart, Germany) in laboratory conditions. Full article