Search Results (24)

To rectify significant heading calculation errors in polarized light navigation for unmanned aerial vehicles (UAVs) under tilted states, this paper proposes a method for compensating horizontal attitude angles based on horizon detection. First, a defogging enhancement algorithm that integrates Retinex theory with dark channel prior is adopted to improve image quality in low-illumination and hazy environments. Second, a dynamic threshold segmentation method in the HSV color space (Hue, Saturation, and Value) is proposed for robust horizon region extraction, combined with an improved adaptive bilateral filtering Canny operator for edge detection, aimed at balancing detail preservation and noise suppression. Then, the progressive probabilistic Hough transform is used to efficiently extract parameters of the horizon line. The calculated horizontal attitude angles are utilized to convert the body frame to the navigation frame, achieving compensation for polarization orientation errors. Onboard experiments demonstrate that the horizontal attitude angle estimation error remains within 0.3°, and the heading accuracy after compensation is improved by approximately 77.4% relative to uncompensated heading accuracy, thereby validating the effectiveness of the proposed algorithm. Full article

This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast, which is equivalent to the pixel informativeness indicator of the image. Proposed preprocessing methods give a delay of no more than 0.03 s due to the use of a pipeline method for data processing. Experimental results show that the horizon clipping algorithm improves fire and smoke detection accuracy by approximately 11%. The best results with the neural network were achieved with YOLO 5m, which yielded an F1 score of 76.75% combined with a processing speed of 45 frames per second. The obtained results differ from existing analogs by utilizing a comprehensive approach to early fire detection, which includes image enhancement and alternative real-time video processing methods. Full article

Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios. Full article

This paper represents observations on detection of Very High Frequency (VHF) anomalous propagation over the area of the Adriatic Sea. During the research campaign, a Software Defined Radio (SDR) Automatic Identification System (AIS) receiver was employed for collection of AIS data packets at a fixed location in the Northern Adriatic. Data were collected during the 24-h period (25 February 2023 15:32 LT to 26 February 2023 15:32 LT), providing information from 115 AIS targets, or 159 965 AIS packets with 54.3% Packet Error Rate (PER), respectively. Subsequent analysis and post-processing of successfully demodulated signals and decoded packets was presented further. In certain instances, the SDR AIS receiver detected, received and decoded data packets from AIS targets distant several orders of magnitude larger than the VHF nominal ranges. To determine the magnitude of line-of-sight and over-the-horizon radio waves propagation, the great circle distances between the SDR AIS receiver antenna and AIS packets’ decoded positions were calculated, revealing hundreds of Nautical Miles (NM). Possible reasons for these occurrences, including tropospheric scattering, diffraction, ionospheric sporadic E layer and refraction were discussed and evaluated, in accordance, among others, with the previous research. By exclusion criteria and neglection of possible causes, it was concluded that the enhanced, over-the-horizon propagation of AIS signals occurred as a result of refraction effects, namely trapping/ducting, subrefraction and superrefraction. Data from nine World Meteorological Organization (WMO) radiosondes surrounding the greater reception area were collected for the same observation periods. Atmospheric profiles were created using Advanced Refractive Effects Prediction System (AREPS) program, and analysed for each individual station measurement. The results confirmed anomalous, over-the-horizon enhanced propagation and their probable origins, i.e., the occurrence of refractive conditions in the atmosphere over the Adriatic Sea area. These findings provide a solid foundation for further research in the area of propagation of VHF signals and their anomalous features caused by the atmospheric phenomenon effects. Full article

A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs. Full article

Tower cranes can cover most of the area of a construction site, which brings significant safety risks, including potential collisions with other entities. To address these issues, it is necessary to obtain accurate and real-time information on the orientation and location of tower cranes and hooks. As a non-invasive sensing method, computer vision-based (CVB) technology is widely applied on construction sites for object detection and three-dimensional (3D) localization. However, most existing methods mainly address the localization on the construction ground plane or rely on specific viewpoints and positions. To address these issues, this study proposes a framework for the real-time recognition and localization of tower cranes and hooks using monocular far-field cameras. The framework consists of four steps: far-field camera autocalibration using feature matching and horizon-line detection, deep learning-based segmentation of tower cranes, geometric feature reconstruction of tower cranes, and 3D localization estimation. The pose estimation of tower cranes using monocular far-field cameras with arbitrary views is the main contribution of this paper. To evaluate the proposed framework, a series of comprehensive experiments were conducted on construction sites in different scenarios and compared with ground-truth data obtained by sensors. The experimental results show that the proposed framework achieves high precision in both crane jib orientation estimation and hook position estimation, thereby contributing to the development of safety management and productivity analysis. Full article

This work aimed to investigate, in detail, the elemental and molecular composition of soil samples collected from the various horizons of an Amazon spodosol profile by combining the atomic technique laser-induced breakdown spectroscopy (LIBS) with two molecular techniques, i.e., Raman and fluorescence spectroscopies. The emission lines of the elements Fe, C, Si, Mg, Al, Ti, Ca, and K with various relative intensities were detected by using LIBS. In particular, C appeared to accumulate in the transition horizon and was proven to be mostly humified by fluorescence analysis. The Raman peaks detected at 465 cm⁻¹ and 800 cm⁻¹ corresponded with the symmetric stretching and bending modes of O-Si-O and Si-OH, respectively. Small shifts toward higher frequencies and slight increases in the width and full width at half maximum (FWHM) of the quartz band at 465 cm⁻¹ appeared in the Tr to K2 horizons, which could be ascribed to a local distortion caused by the high contents of Al of kaolinite mineral phases, as also shown by the LIBS data. Thus, a small amount of kaolinite mineral phase and K measured by LIBS would be present also in the A1 to E2 horizons. The lifetime fluorescence was almost constant in the surface and middle horizons, whereas it increased sharply in the transition horizon and then decreased slightly in the kaolin horizons, which confirmed the presence of more humified recalcitrant organic matter in deeper soil horizons. In conclusion, the combined use of the three spectroscopic techniques appeared to be a very promising approach for studying Amazon soils. Full article

This paper reports on a compact and lightweight sensor for analysis of gases/vapors by means of a MEMS-based pre-concentrator coupled to a miniaturized infrared absorption spectroscopy (IRAS) module. The pre-concentrator was utilized to sample and trap vapors in a MEMS cartridge filled with sorbent material and to release them once concentrated by fast thermal desorption. It was also equipped with a photoionization detector for in-line detection and monitoring of the sampled concentration. The vapors released by the MEMS pre-concentrator are injected into a hollow fiber, which acts as the analysis cell of the IRAS module. The miniaturized internal volume of the hollow fiber of about 20 microliters keeps the vapors concentrated for analysis, thus allowing measurement of their infrared absorption spectrum with a signal to noise ratio high enough to identify the molecule, despite the short optical path, starting from sampled concentration in air down to parts per million. Results obtained for ammonia, sulfur hexafluoride, ethanol and isopropanol are reported to illustrate the sensor detection and identification capability. A limit of identification (LoI) of about 10 parts per million was validated in the lab for ammonia. The lightweight and low power consumption design of the sensor allowed operation onboard unmanned aerial vehicles (UAVs). The first prototype was developed within the EU Horizon 2020 project ROCSAFE for the remote assessment and forensic examination of a scene in the aftermath of industrial or terroristic accidents. Full article

The sampling protocols for the official control of the levels of mycotoxins in foodstuffs are very costly and time-consuming. More efforts are needed to implement alternative sampling plans able to support official control, or to adapt the current ones. The aim of the research carried out within the European Horizon 2020 MycoKey project was to evaluate the applicability at industrial scale of the dust sampling approach to detect multiple mycotoxins in grains. To this end, two trials were performed on an EU industrial site: (i) control of the unloading of wheat from train wagons; (ii) control of the unloading of wheat from trucks. In line with previous studies, the MycoKey results indicated that dust sampling and mycotoxin analysis represent a fitness for purpose approach for non–destructive and rapid identification of wheat commodities compliant to the maximum permitted levels. Based on reviewed and newly generated results, this article discusses potential applications and limits of the dust sampling methodology, identifying future research needs. Full article

Unpredictable fluctuant passenger flow usually exists in urban metro operations. In this situation, traditional predetermined metro timetables cannot always meet the variation of passenger flow, and thus the service quality of the metro system could be affected profoundly. In this paper, by introducing an innovative metro operation method without timetables, we develop a nonlinear integer programming model to continually optimise the train operation to deal with detected real-time passenger flow variations. We aim to minimise the total passenger waiting time in the research time horizon under the vehicle number constraint. A modified genetic algorithm integrated with a macroscopic metro simulator is adopted to solve the proposed model. A case study based on the Beijing Metro Line 19 is implemented to provide a quantitative result for evaluating the proposed passenger flow-oriented metro operation method without timetables. Compared to traditional timetable-based metro operation, the method could significantly improve the metro operation’s flexibility and the quality of services. Full article

The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient C_mean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient C_mean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R² = 0.841) and the thickness of the humus horizon (R² = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS. Full article

Sea horizon line (SHL) detection plays a pivotal role in the computational performance improvement of computer applications for the maritime environment by dividing the image into sea and sky regions. This division isolates the region of interest and reduces the computational cost of further processing. Testing and performance evaluation of SHL detection methods require a robust image dataset covering the maritime environment’s features at different geographical, seasonal, and maritime conditions. However, publicly available maritime image datasets are developed under a limited environment with slight-to-moderate variations in maritime features. This article proposes a novel sea image dataset that fills this gap by incorporating various geographical, seasonal, and maritime features. Across West Malaysia, one offshore and four geographically separated onshore locations were selected. On ten different occasions, field observations were recorded using a visual-range optical sensor and weather station. The data collection experiments were conducted between February 2020 until April 2021. The collected data were preprocessed and SHL images were selected based on their high feature diversity. Manual SHL annotation was applied on images, and a ground truth matrix was generated, which serves as a performance benchmark for SHL detection methods. As a result, the dataset presents 2673 high-definition (1920 × 1080 pixels) RGB images having a combination of 36 different geographical, seasonal, and maritime features to test and evaluate computer vision-based SHL detection methods. Full article

Studies on virtual-to-realistic image style transfer have been conducted to minimize the difference between virtual simulators and real-world environments and improve the training of artificial intelligence (AI)-based autonomous driving models using virtual simulators. However, when applying an image style transfer network architecture that achieves good performance using land-based data for autonomous vehicles to marine data for autonomous vessels, structures such as horizon lines and autonomous vessel shapes often lose their structural consistency. Marine data exhibit substantial environmental complexity, which depends on the size, position, and direction of the vessels because there are no lanes such as those for cars, and the colors of the sky and ocean are similar. To overcome these limitations, we propose a virtual-to-realistic marine image style transfer method using horizon-targeted loss for marine data. Horizon-targeted loss helps distinguish the structure of the horizon within the input and output images by comparing the segmented shape. Additionally, the design of the proposed network architecture involves a one-to-many style mapping technique, which is based on the multimodal style transfer method to generate marine images of diverse styles using a single network. Experiments demonstrate that the proposed method preserves the structural shapes on the horizon more accurately than existing algorithms. Moreover, the object detection accuracy using various augmented training data was higher than that observed in the case of training using only virtual data. The proposed method allows us to generate realistic data to train AI models of vision-based autonomous vessels by actualizing and augmenting virtual images acquired from virtual autonomous vessel simulators. Full article

We place constraints on DM sterile neutrino scalar decay production (SDP) assuming that sterile neutrinos representa fraction from the total Cold Dark Matter energy density. For the cosmological analysis we complement the CMB anisotropy measurements with CMB lensing gravitational potential measurements, that are sensitive to the DM distribution to high redshifts and with the cosmic shear data that constrain the gravitational potential at lower redshifts than CMB. We also use the most recent low-redshift BAO measurements that are insensitive to the non-linear effects, providing robust geometrical tests. We show that our datasets have enough sensitivity to constrain the sterile neutrino mass $m_{{\nu }_s}$ and the mass fraction $f_S$ inside the co-moving free-streaming horizon. We find that the best fit value $m_{{\nu }_s}=7.88\pm 0.73$ keV (68% CL) is in the parameter space of interest for DM sterile neutrino decay interpretation of the 3.5 keV X-ray line and that $f_S=0.86\pm 0.07$ (68% CL) is in agreement with the upper limit constraint on $f_S$ from the X-ray non-detection and Ly-$\alpha$ forest measurements that rejects $f_S=1$ at 3$\sigma$. However, we expect that the future BAO and weak lensing surveys, such as EUCLID, will provide much more robust constraints. Full article

Horizon line detection is an important prerequisite for numerous tasks including the automatic estimation of the unknown camera parameters for images taken in mountainous terrain. In contrast to modern images, historical photographs contain no color information and have reduced image quality. In particular, missing color information in combination with high alpine terrain, partly covered with snow or glaciers, poses a challenge for automatic horizon detection. Therefore, a robust and accurate approach for horizon line detection in historical monochrome images in mountainous terrain was developed. For the detection of potential horizon pixels, an edge detector is learned based on the region covariance as texture descriptor. In combination with shortest path search the horizon in monochrome images is accurately detected. We evaluated our approach on 250 selected historical monochrome images in average dating back to 1950. In 85% of the images the horizon was detected with an error less than 10 pixels. In order to further evaluate the performance, an additional dataset consisting of modern color images was used. Our method, using only grayscale information, achieves comparable results with methods based on color information. In comparison with other methods using only grayscale information, accuracy of the detected horizons is significantly improved. Furthermore, the influence of color, choice of neighborhood for the shortest path calculation, and patch size for the calculation of the region covariance were investigated. The results show that both the availability of color information and increasing the patch size for the calculation of the region covariance improve the accuracy of the detected horizons. Full article