Search Results (1,139)

Fishways can effectively validate the effectiveness and rationality of their construction, optimize operational modes, and achieve intelligent scientific management through fish species detection. Traditional fish species detection methods for fishways are unsuitable due to inefficiency and disruption of the fish ecological environment. Therefore, combining cameras with target detection technology provides a better solution. However, challenges include the limited computational power of onsite equipment, the complexity of model deployment, low detection accuracy, and slow detection speed, all of which are significant obstacles. This paper proposes a fish detection model for accurate and efficient fish detection. Firstly, the backbone network integrates FasterNet-Block, C2f, and an efficient multi-scale EMA attention mechanism to address attention dispersion problems during feature extraction, delivering real-time object detection across different scales. Secondly, the Neck introduces a novel architecture to enhance feature fusion by integrating the RepBlock and BiFusion modules. Finally, the performance of the fish detection model is demonstrated based on the Fish26 dataset, in which the detection accuracy, computational cost, and parameter count are significantly optimized by 1.7%, 23.4%, and 24%, respectively, compared to the state-of-the-art model. At the same time, we installed detection devices in a specific fishway and deployed the proposed method within these devices. We collected data on four fish species passing through the fishway to create a dataset and train the model. The results of the practical application demonstrated superior fish detection capabilities, with rapid detection ability achieved while minimizing resource usage. This validated the effectiveness of the proposed method for equipment deployment in real-world engineering environments. This marks a shift from traditional manual detection to intelligent fish species detection in fishways, promoting water resource utilization and the protection of fish ecological environments. Full article

Promoting alternative modes of transportation such as cycling represents a valuable strategy to minimize environmental impacts, as confirmed in the main targets set out by the European Commission. In this regard, in cities throughout the world, there has been a significant increase in the construction of bicycle paths in recent years, requiring effective maintenance strategies to preserve their service levels. The continuous monitoring of road networks is required to ensure the timely scheduling of optimal maintenance activities. This involves regular inspections of the road surface, but there are currently no automated systems for monitoring cycle paths. In this study, an integrated monitoring and assessment system for cycle paths was developed exploiting Raspberry Pi technologies. In more detail, a low-cost Inertial Measurement Unit (IMU), a Global Positioning System (GPS) module, a magnetic Hall Effect sensor, a camera module, and an ultrasonic distance sensor were connected to a Raspberry Pi 4 Model B. The novel system was mounted on a e-bike as a test vehicle to monitor the road conditions of various sections of cycle paths in Rome, characterized by different pavement types and decay levels as detected using the whole-body vibration $a_{wz}$ index (ISO 2631 standard). Repeated testing confirmed the system’s reliability by assigning the same vibration comfort class in 74% of the cases and an adjacent one in 26%, with an average difference of 0.25 m/s², underscoring its stability and reproducibility. Data post-processing was also focused on integrating user comfort perception with image data, and it revealed anomaly detections represented by numerical acceleration spikes. Additionally, data positioning was successfully implemented. Finally, $a_{wz}$ measurements with GPS coordinates and images were incorporated into a Geographic Information System (GIS) to develop a database that supports the efficient and comprehensive management of surface conditions. The proposed system can be considered as a valuable tool to assess the pavement conditions of cycle paths in order to implement preventive maintenance strategies within budget constraints. Full article

A drawback of fringe projection profilometry (FPP) is that it is still a challenge to perform efficient and accurate high-resolution absolute phase recovery with only a single measurement. This paper proposes a single-model self-recovering fringe projection absolute phase recovery method based on deep learning. The built Fringe Prediction Self-Recovering network converts a single fringe image acquired by a camera into four single mode self-recovering fringe images. A self-recovering algorithm is adopted to obtain wrapped phases and fringe grades, realizing high-resolution absolute phase recovery from only a single shot. Low-cost and efficient dataset preparation is realized by the constructed virtual measurement system. The fringe prediction network showed good robustness and generalization ability in experiments with multiple scenarios using different lighting conditions in both virtual and physical measurement systems. The absolute phase recovered MAE in the real physical measurement system was controlled to be 0.015 rad, and the reconstructed point cloud fitting RMSE was 0.02 mm. It was experimentally verified that the proposed method can achieve efficient and accurate absolute phase recovery under complex ambient lighting conditions. Compared with the existing methods, the method in this paper does not need the assistance of additional modes to process the high-resolution fringe images directly. Combining the deep learning technique with the self-recovering algorithm simplified the complex process of phase retrieval and phase unwrapping, and the proposed method is simpler and more efficient, which provides a reference for the fast, lightweight, and online detection of FPP. Full article

An open question for the biomechanical research community is accurate estimation of the volume and mass of each body segment of the human body, especially when indirect measurements are based on biomechanical modeling. Traditional methods involve the adoption of anthropometric tables, which describe only the average human shape, or manual measurements, which are time-consuming and depend on the operator. We propose a novel method based on the acquisition of a 3D scan of a subject’s body, which is obtained using a consumer-end RGB-D camera. The body segments’ separation is obtained by combining the body skeleton estimation of BlazePose with a biomechanical-coherent skeletal model, which is defined according to the literature. The volume of each body segment is computed using a 3D Monte Carlo procedure. Results were compared with manual measurement by experts, anthropometric tables, and a model leveraging truncated cone approximations, showing good adherence to reference data with minimal differences (ranging from $+0.5$ to $-1.0$ dm³ for the upper limbs, $-0.1$ to $-4.2$ dm³ for the thighs, and $-0.4$ to $-2.3$ dm³ for the shanks). In addition, we propose a novel indicator based on the computation of equivalent diameters for each body segment, highlighting the importance of gender-specific biomechanical models to account for the chest and pelvis areas of female subjects. Full article

With the rapid development of hyperspectral applications using unmanned aerial vehicles (UAVs), the traditional assumption that ground objects exhibit Lambertian reflectance is no longer sufficient to meet the high-precision requirements for quantitative inversion and airborne hyperspectral data applications. Therefore, it is necessary to establish a hyperspectral bidirectional reflectance distribution function (BRDF) model suitable for the area of imaging. However, obtaining multi-angle information from UAV push-broom hyperspectral data is difficult. Achieving uniform push-broom imaging and flexibly acquiring multi-angle data is challenging due to spatial distortions, particularly under heightened roll or pitch angles, and the need for multiple flights; this extends acquisition time and exacerbates uneven illumination, introducing errors in BRDF model construction. To address these issues, we propose leveraging the advantages of multi-spectral cameras, such as their compact size, lightweight design, and high signal-to-noise ratio (SNR) to reconstruct hyperspectral multi-angle data. This approach enhances spectral resolution and the number of bands while mitigating spatial distortions and effectively captures the multi-angle characteristics of ground objects. In this study, we collected UAV hyperspectral multi-angle data, corresponding illumination information, and atmospheric parameter data, which can solve the problem of existing BRDF modeling not considering outdoor ambient illumination changes, as this limits modeling accuracy. Based on this dataset, we propose an improved Walthall model, considering illumination variation. Then, the radiance consistency of BRDF multi-angle data is effectively optimized, the error caused by illumination variation in BRDF modeling is reduced, and the accuracy of BRDF modeling is improved. In addition, we adopted Transformer for spectral reconstruction, increased the number of bands on the basis of spectral dimension enhancement, and conducted BRDF modeling based on the spectral reconstruction results. For the multi-level Transformer spectral dimension enhancement algorithm, we added spectral response loss constraints to improve BRDF accuracy. In order to evaluate BRDF modeling and quantitative application potential from the reconstruction results, we conducted comparison and ablation experiments. Finally, we solved the problem of difficulty in obtaining multi-angle information due to the limitation of hyperspectral imaging equipment, and we provide a new solution for obtaining multi-angle features of objects with higher spectral resolution using low-cost imaging equipment. Full article

With the intensification of global climate change, extreme precipitation events are occurring more frequently, making the monitoring and management of urban flooding a critical global issue. Urban surveillance camera sensor networks, characterized by their large-scale deployment, rapid data transmission, and low cost, have emerged as a key complement to traditional remote sensing techniques. These networks offer new opportunities for high-spatiotemporal-resolution urban flood monitoring, enabling real-time, localized observations that satellite and aerial systems may not capture. However, in low-light environments—such as during nighttime or heavy rainfall—the image features of flooded areas become more complex and variable, posing significant challenges for accurate flood detection and timely warnings. To address these challenges, this study develops an imaging model tailored to flooded areas under low-light conditions and proposes an invariant feature extraction model for flooding areas within surveillance videos. By using extracted image features (i.e., brightness and invariant features of flooded areas) as inputs, a deep learning-based flood segmentation model is built on the U-Net architecture. A new low-light surveillance flood image dataset, named UWs, is constructed for training and testing the model. The experimental results demonstrate the efficacy of the proposed method, achieving an mRecall of 0.88, an mF1_score of 0.91, and an mIoU score of 0.85. These results significantly outperform the comparison algorithms, including LRASPP, DeepLabv3+ with MobileNet and ResNet backbones, and the classic DeepLabv3+, with improvements of 4.9%, 3.0%, and 4.4% in mRecall, mF1_score, and mIoU, respectively, compared to Res-UNet. Additionally, the method maintains its strong performance in real-world tests, and it is also effective for daytime flood monitoring, showcasing its robustness for all-weather applications. The findings of this study provide solid support for the development of an all-weather urban surveillance camera flood monitoring network, with significant practical value for enhancing urban emergency management and disaster reduction efforts. Full article

HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. Full article

Common eastern bumble bees (Bombus impatiens Cresson) play an essential role in pollinating lowbush blueberries (LB) in northern Quebec, but their costs and the suboptimal weather conditions during pollination highlight the need to find appropriate hive management strategies. A study was conducted in a LB field in Saguenay (Québec, Canada) focusing on the effects of hive insulation (I+ and I−), heating (H+ and H−), and placement in a single-row tree line windbreak. High-definition time-lapse cameras monitored hive activities and bumble bee foraging behaviors. We found that the conventional management of placing hives in full sun without insulation (I−) resulted in the lowest levels of bumble bee foraging activity and overall hive traffic. Placing bumble bee hives against a windbreak resulted in the highest numbers of bees entering hives with pollen (+156%), leaving hives (+69%), and overall hive traffic (+76%). Insulating hives with extruded polystyrene foam gave intermediate results, with a 105% increase in foraging activity compared to the conventional management method (I−H−). Interestingly, placing hives on seedling mats to maintain colony temperatures above 15 °C (H+) tended to decrease foraging activity and overall hive traffic. Our results show that strategic placement of bumble bee hives against windbreaks can significantly increase the activity of Bombus workers from those hives and can be used as a simple, low-cost, and efficient bumble bee hive management method by LB growers. Full article

This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial–temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas—the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors’ behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process of redesigning the WUT campus, but also to support the process of discussion and activation of the community in the development of deliberative democracy and participatory shaping of space in general. Full article

In this study, we present a gesture-controlled robotic arm system for small assembly lines. Robotic arms are extensively used in industrial applications; however, they typically require special treatment and qualified personnel to set up and operate them. Towards this end, hand gestures can provide a natural way for human–robot interaction, providing a straightforward means for control without the need for significant training of the operators. Our goal is to develop a safe, low-cost, and user-friendly system for environments that often involve non-repetitive and custom automation processes, such as in small factory setups. Our system estimates the 3D position of the user’s joints in real time with the help of AI and real-world data provided by an RGB-D camera. Then, joint coordinates are translated into the robotic arm’s desired poses in a simulated environment (ROS), thus achieving gesture control. Through the experiments we conducted, we show that the system provides the performance required to control a robotic arm effectively and efficiently. Full article

Background: Occupational low back pain (LBP) is a pervasive health issue that significantly impacts productivity and contributes to work-related musculoskeletal disorders (WMSDs). Inadequate lifting postures are a primary, modifiable risk factor associated with LBP, making early detection of unsafe practices crucial to mitigating occupational injuries. Our study aims to address these limitations by developing a markerless, smartphone-based camera system integrated with a deep learning model capable of accurately classifying lifting postures. Material and Method: We recruited 50 healthy adults who participated in lifting tasks using correct and incorrect postures to build a robust dataset. Participants lifted boxes of varying sizes and weights while their movements were recorded from multiple angles and heights to ensure comprehensive data capture. We used the OpenPose algorithm to detect and extract key body points to calculate relevant biomechanical features. These extracted features served as inputs to a bidirectional long short-term memory (LSTM) model, which classified lifting postures into correct and incorrect categories. Results: Our model demonstrated high classification accuracy across all datasets, with accuracy rates of 96.9% for Tr, 95.6% for the testing set, and 94.4% for training. We observed that environmental factors, such as camera angle and height, slightly influenced the model’s accuracy, particularly in scenarios where the subject’s posture partially occluded key body points. Nonetheless, these variations were minor, confirming the robustness of our system across different conditions. Conclusions: This study demonstrates the feasibility and effectiveness of a smartphone camera and AI-based system for lifting posture classification. The system’s high accuracy, low setup cost, and ease of deployment make it a promising tool for enhancing workplace ergonomics. This approach highlights the potential of artificial intelligence to improve occupational safety and underscores the relevance of affordable, scalable solutions in the pursuit of healthier workplaces. Full article

Despite notable advancements in the development of borate materials, improving their luminescent efficiency remains an important focus in materials research. The synthesis of lanthanum borates (LaBO₃), doped and co-doped with europium (Eu³⁺) and dysprosium (Dy³⁺), by the solid-state method, has demonstrated significant potential to address this challenge due to their unique optical properties. These materials facilitate efficient energy transfer from UV-excited host crystals to trivalent rare-earth activators, resulting in stable and high-intensity luminescence. To better understand their structural and vibrational characteristics, Fourier transform infrared (FTIR) spectroscopy and Raman spectroscopy were employed to identify functional groups and molecular vibrations in the synthesized materials. Additionally, X-ray diffraction (XRD) analysis was conducted to determine the crystalline structure and phase composition of the samples. All observed transitions of Eu³⁺ and Dy³⁺ in the excitation and emission spectra were systematically analyzed and identified, providing a comprehensive understanding of their behavior. Although smartphone cameras exhibit non-uniform spectral responses, their integration into this study highlights distinct advantages, including contactless interrogation, effective UV excitation suppression, and real-time spectral analysis. These capabilities enable practical and portable fluorescence sensing solutions for applications in healthcare, environmental monitoring, and food safety. By combining advanced photonic materials with accessible smartphone technology, this work demonstrates a novel approach for developing low-cost, scalable, and innovative sensing platforms that address modern technological demands. Full article

The capability of low-cost photogrammetric systems to provide live six degrees of freedom (6DoF) tracking of multiple objects offers great value in providing high-quality and consistent part production by automated manufacturing systems. However, monitoring of high-speed components, such as cutting heads, presents several unique challenges that existing systems struggle to meet. The solution given here uses a small number of short-exposure imaging sensors coupled with high-intensity lighting and retrorefective markers to allow for high-speed capture. The use of an initial characterization process carried out using IDEKO’s VSET© system is followed by live object tracking in bespoke image processing software running on a consumer-grade computer. Once this system is in use, it can simultaneously capture images of multiple markers in less than 0.1 milliseconds and use these to determine the 6DoF of the objects that the markers define. 6DoF recalculation of all objects within each measurement instance makes the system resilient to large movements, object occlusion, and sensor relocation. Feasibility tests of a four-camera system as a machine characterization tool tracking a cutting tool spinning at up to 3000 rpm across a volume of 1 m³ achieved a mean reference marker agreement between tool poses of 2.5 µm with markers moving at up to 17.5 ms⁻¹. Given good photogrammetric geometry, 6DoF parameters of the spinning tool were determined to standard deviations of 37.7 µm and 0.086°. Full article

The aquatic plant species Eichhornia crassipes, commonly known as water hyacinth, is indigenous to South America and is considered an invasive species. The invasive water hyacinth has caused significant economic and ecological damage by preventing sunlight from penetrating the surface of the water, resulting in the loss of aquatic life. To quantify the invasiveness and address the issue of accurately identifying plant species, water hyacinths have prompted numerous researchers to propose approaches to detect regions occupied by water hyacinths. One such solution involves the utilization of multispectral imaging which obtain detailed information about plant species based on the surface reflectance index. This is achieved by analyzing the intensity of light spectra at different wavelengths emitted by each plant. However, the use of multispectral imagery presents a potential challenge since there are various spectral indices that can be used to capture different information. Despite the high accuracy of these multispectral images, there remains a possibility that plants similar to water hyacinths may be misclassified if the right spectral index is not chosen. Considering this challenge, the objective of this research is to develop a low-cost multispectral camera capable of capturing multispectral images. The camera will be equipped with two infrared light spectrum filters with wavelengths of 720 and 850 nanometers, respectively, as well as red, blue, and green light spectrum filters. Additionally, the implementation of the U-Net architecture is proposed for semantic segmentation to accurately identify water hyacinths, as well as other classes such as lakes and land. An accuracy rate of 96% was obtained for the identification of water hyacinths using data captured by an autonomous drone constructed in the laboratory flying at an altitude of 10 m. We also analyzed the contribution each of the infrared layers to the camera’s spectrum setup. Full article

Aerial optical cameras are the primary method for capturing high-resolution images to produce large-scale mapping products. To improve aerial photography efficiency, multiple cameras are often used in combination to generate large-format virtual central projection images. This paper presents a high-precision method for directly transforming raw images obtained from a dual-camera system mounted at an oblique angle into virtual central projection images, thereby enabling the construction of low-cost, large-format aerial camera systems. The method commences with an adaptive sub-block in the overlapping regions of the raw images to extract evenly distributed feature points, followed by iterative relative orientation to improve accuracy and reliability. A global projection transformation matrix is constructed, and the sigmoid function is employed as a weighted distance function for image stitching. The results demonstrate that the proposed method produces more evenly distributed feature points, higher relative orientation accuracy, and greater reliability. Simulation analysis of image overlap indicates that when the overlap exceeds 7%, stitching accuracy can be better than 1.25 μm. The aerial triangulation results demonstrate that the virtual central projection images satisfy the criteria for the production of 1:1000 scale mapping products. Full article