Poor Visibility Conditions Research Articles

Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging

Abstract. The surface temperature of snow cover is a key variable, as it provides information about the current state of the snowpack, helps predict its future evolution, and enhances estimations of the snow water equivalent. Although satellites are often used to measure the surface temperature despite the difficulty of retrieving accurate surface temperatures from space, calibration–validation datasets over snow-covered areas are scarce. We present a dataset of extensive measurements of the surface radiative temperature of snow acquired with an uncooled thermal-infrared (TIR) camera. The set accuracy goal is 0.7 K, which is the radiometric accuracy of the TIR sensor of the future CNES/ISRO TRISHNA mission. TIR images have been acquired over two winter seasons, November 2021 to May 2022 and February to May 2023, at the Col du Lautaret, 2057 m a.s.l. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration with rough thermal regulation (7–39 °C). An improved setup with a stabilized internal temperature was developed for the second campaign, and comprehensive laboratory experiments were carried out in order to characterize the physical properties of the components of the TIR camera and its calibration. Thorough processing, including radiometric processing, orthorectification, and a filter for poor-visibility conditions due to fog or snowfall, was performed. The result is two winter season time series of 130 019 maps of the surface radiative temperature of snow with meter-scale resolution over an area of 0.5 km2. The validation was performed against precision TIR radiometers. We found an absolute accuracy (mean absolute error, MAE) of 1.28 K during winter 2021–2022 and 0.67 K for spring 2023. The efforts to stabilize the internal temperature of the TIR camera therefore led to a notable improvement of the accuracy. Although some uncertainties persist, particularly the temperature overestimation during melt, this dataset represents a major advance in the capacity to monitor and map surface temperature in mountainous areas and to calibrate–validate satellite measurements over snow-covered areas of complex topography. The complete dataset is provided at https://doi.org/10.57932/8ed8f0b2-e6ae-4d64-97e5-1ae23e8b97b1 (Arioli et al., 2024a) and https://doi.org/10.57932/1e9ff61f-1f06-48ae-92d9-6e1f7df8ad8c (Arioli et al., 2024b).

Filament-induced breakdown spectroscopy of solids through highly scattering media.

Ultrafast laser pulse filamentation in the air can be used for remote sensing by exciting a characteristic optical emission, which is usually referred to as filamentation-induced breakdown spectroscopy. In environments that impede light propagation, such as fog, haze, or clouds, scattering makes it challenging to propagate laser beams and retrieve generated optical signatures. We demonstrate the effectiveness of laser filamentation for simultaneously clearing the path for intense femtosecond pulse propagation in a highly scattering medium, generation of luminous plasma on a solid target, and counter-propagation of a characteristic spectroscopic signal over a cleared channel along the filament path. In a dense cloud, the counter-propagating signal predominantly transits the cleared on-axis path but is highly affected by the negative thermal lensing of a Gaussian beam. These insights enhance our understanding of laser filamentation in atmospheric sensing and could substantially improve remote detection capabilities in poor visibility conditions.

A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions

Single-file movement of pedestrians at different visibility levels

Previous studies indicate that the movement of pedestrians under poor visibility conditions is significantly different from that under good visibility conditions. To investigate the influence of visibility factor on the longitudinal movement of pedestrian, series of single-file experiments were performed under the quantitative control of visibility level. Based on the extracted trajectories, typical evacuation behavior and movement characteristics of pedestrians under different visibility conditions are explored. It is found that the lateral swing amplitude of pedestrians during movement is within 20 cm under low densities while it is close to 30 cm at high densities. The free velocity under the light transmission of 100 %, 2.2 %, 0.5 % and 0.1 % is 0.856 m/s, 0.823 m/s, 0.741 m/s and 0.712 m/s respectively. Individuals cannot accurately perceive the surrounding situations with the worsening of visual condition, therefore the movement speed decreases under the poor visibilities. Besides, two regimes including the constrained regime and free regime are identified in the headway-velocity relation at different visibility levels. The maximum specific flow under the light transmission of 100 %, 2.2 %, 0.5 % and 0.1 % is 0.70 s−1, 0.67 s−1, 0.62 s−1 and 0.55 s−1 respectively. The study is helpful to reveal the pedestrian dynamics of single-file movement under the limited visibility conditions.

Efficiency of Using an Automated Monitoring System to Prevent and Protect the Population from Avalanches

Automated avalanche detection is an important tool for predicting avalanches and evaluating the effectiveness of anti-avalanche measures in poor visibility conditions. In this paper, we have developed and tested a decision support system for predicting avalanches based on data from automatic weather stations. Annual observations of avalanches and weather in the mountainous regions of Kazakhstan (Altai, Dzungarian and Zailiyskiy Alatau) are processed and analyzed to identify meteorological factors that are important for the formation of avalanches. The current snow depth and precipitation on previous days of avalanches are the most important and are used to predict the likelihood of avalanches on the test data. The study shows that the developed system, which captures data on snow depth, can be used with a high degree of avalanche prediction probability. At the same time, the presented model has limitations and, therefore, is recommended as a warning system for timely decision making. The possibility of introducing and applying new methods of monitoring avalanches, used in foreign countries, by the avalanche service of the Republic of Kazakhstan is considered.

3D Heads-up digital filters for cataract surgery and corneal transplantation.

To describe the advantages of Ngenuity 3D digital filters for enhancing visualization during cataract and corneal transplant surgery. All surgeries were performed by the same experienced surgeon (L.M.) using the Ngenuity 3D heads-up visualization system connected to a microscope. Images were simultaneously captured with and without the filter in each of the following phases: endothelial evaluation, capsulorhexis, capsule rupture, vitreous leak, cortex removal, visco removal, corneal suture, descemetorhexis, DMEK graft preparation and insertion. In cataract surgery, green and monochrome filters facilitate capsulorhexis in difficult situations by highlighting the anterior capsule and can improve visualization of a posterior capsular tear. The enhanced contrast of the monochrome filter is also useful for cortex and viscoelastic removal. In corneal surgery, the green filter highlights the rim of the DMEK graft during tissue preparation, the yellow filter enhances the contrast of the stained DMEK graft in the anterior chamber, the monochrome filter simplifies descemetorhexis by improving visualization of the Descemet/endothelial layer and allows a better view in red saturated images while performing sutures. Ngenuity digital filters have the potential to enhance tissue visualization during cataract and corneal surgeries, especially in poor visibility conditions.

Safety Helmet for Miners

Abstract: The safety helmets basically aim at safety of miners reducing the risks of accidents. It also highlights the hazardous environments of mining and potential dangers that the workers face while working in mines. Safety helmets will be containing infrared sensors which keep track of the environment of the miner. It highlights the inherent risks miners face, including poor visibility, potential collisions, and hazardous conditions. The helmets are capable of alerting the miners from dangers. Infrared sensors can sense the heat signatures and obstacles in real-time resulting in spatial awareness. The helmets also consist of Arduino to enable real-time data processing and communication integration. The helmet also consists of LoRa which provide wireless communication between helmets and receiver ends, which would notify them in case of any emergency. The abstract concludes by enhancing the importance of IR-sensors, LoRa and Arduino which interfaces all of the above equipment.

Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars

Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) marred with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.

Assessment of Pilots’ Cognitive Competency Using Situation Awareness Recognition Model Based on Visual Characteristics

Visual characteristics have the potential to assess the navigational proficiency of ship pilots. A precise assessment of ship piloting competence is imperative to mitigate human errors in piloting. An exhaustive examination of cognitive capabilities plays a pivotal role in developing an enhanced and refined system for classifying, selecting, and training ship piloting proficiency. Insufficiency in situation awareness (SA), denoting the cognitive underpinning of hazardous behaviors among pilots, may lead to subpar performance in ship pilotage when faced with adverse conditions. To address this issue, we propose an SA recognition model based on the random forest-support vector machine (RF-SVM) algorithm, which utilizes wearable eye-tracking technology to detect pilots’ at-risk cognitive state, specifically low-SA levels. We rectify the relative error (RE) and root mean square error (RMSE) and employ principal component analysis (PCA) to enhance the RF algorithm, optimizing the combination of salient features in greater depth. Through the utilization of these feature combinations, we construct a SVM algorithm using the most suitable variables for SA recognition. Our proposed RF-SVM algorithm is compared to RF or SVM alone, and it achieves the highest accuracy in recognizing at-risk cognitive states under poor visibility conditions (an improvement of 86.79% to 93.43% in accuracy). Taken collectively, the present findings offer vital technical support for developing a technique-based intelligent system for adaptively evaluating the cognitive accomplishment of pilots. Furthermore, they establish the groundwork and framework for the surveillance of cognitive processes and capabilities in marine pilotage operations within China.

Correction: Yang et al. Robust Visual Recognition in Poor Visibility Conditions: A Prior Knowledge-Guided Adversarial Learning Approach. Electronics 2023, 12, 3711

In the published publication [...]

BER performance analysis of MWIR and SWIR FSO links utilizing aperture averaging technique considering turbulence and various visibility situations with pointing errors

Abstract The atmospheric turbulence and various weather situations deteriorate the system performance of free space optical (FSO) communication. In this article, the ‘bit error rate’ (BER) performance of the ‘mid wavelength infrared’ (MWIR) and ‘short wavelength infrared’ (SWIR) FSO link has been evaluated utilizing the aperture averaging approach with pointing errors in the presence of turbulence and different visibility scenario like fog, haze, clear weather, etc. The transmitter and receiver are 6 km apart, the modulation method is on off keying (OOK), and several aperture sizes at the receiver side have been taken into consideration for the aperture averaging approaches, such as 100 mm, 150 mm, and 200 mm. Additionally, for analysis of the pointing error of both the wavelengths of the FSO link, different beam divergence angle of the transmitter (laser) has been considered. The BER performance is improved with optimum beam divergence angles. The optimization of the divergence angle is independent of wavelengths and visibility conditions. However, the BER has increased more below the optimized divergence angle compared to a larger angle. The MWIR link performs well in poor visibility conditions like fog, whereas the SWIR link fails for the same link distance in both conditions with and without pointing errors.

Analysis of Pilot Distance Estimation in Different Lighting and Visibility Conditions

Several studies on distance and size estimation have focused on normal and night vision goggles (NVGs), but none of them have been performed during the twilight period. Hence, in this study, distance was estimated for the first time during nautical twilight. According to the findings, the accuracy of distance estimation reduces as visibility decreases and is restricted. When compared with Day Limited Helmet Mounted Display Vision (M = 5.27, SD = .59), Twilight Normal Vision (M = 5.33, SD = .69) and Twilight Helmet Mounted Display Vision (M = 5.20, SD = .61), NVG (M = 4.79, SD = .57) appears to have a lower error rate. In this study, distance was estimated considering objects determined during the helicopter flight by the pilots in different visibility conditions, which are significant in the field of aviation. This work is unique owing to its coverage of helicopter pilots and the estimation during the twilight period. In view of our findings, it may be reasonable to postpone the planned helicopter flights during poor visibility conditions.

A novel deep learning based underwater image de-noising and detecting suspicious object

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects.

Robust Visual Recognition in Poor Visibility Conditions: A Prior Knowledge-Guided Adversarial Learning Approach

Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor visibility conditions through techniques such as image restoration, data augmentation, and unsupervised domain adaptation, these efforts are predominantly confined to specific scenarios and fail to address multiple poor visibility scenarios encountered in real-world settings. Furthermore, the valuable prior knowledge inherent in poor visibility images is seldom utilized to aid in resolving high-level computer vision tasks. In light of these challenges, we propose a novel deep learning paradigm designed to bolster the robustness of object recognition across diverse poor visibility scenes. By observing the prior information in diverse poor visibility scenes, we integrate a feature matching module based on this prior knowledge into our proposed learning paradigm, aiming to facilitate deep models in learning more robust generic features at shallow levels. Moreover, to further enhance the robustness of deep features, we employ an adversarial learning strategy based on mutual information. This strategy combines the feature matching module to extract task-specific representations from low visibility scenes in a more robust manner, thereby enhancing the robustness of object recognition. We evaluate our approach on self-constructed datasets containing diverse poor visibility scenes, including visual blur, fog, rain, snow, and low illuminance. Extensive experiments demonstrate that our proposed method yields significant improvements over existing solutions across various poor visibility conditions.

Analysis of human errors in maritime accidents: A Bayesian spatial multinomial logistic model

Considering the unobserved spatial heterogeneity, this study aims to build a Bayesian spatial multinomial logistic (BSMNL) model by utilizing the geographic information from historical maritime accidents. The proposed BSMNL model can be applied to investigate the determinants of human errors involved in maritime accidents. Compared to the traditional multinomial logistic (MNL) model, the proposed BSMNL model produces a more accurate estimate of the effects of environmental and accident factors on the occurrence likelihood of human errors in maritime accidents. Results show that accidents involving cargo and container ships; tankers carrying liquefied natural gas (LNG), liquefied petroleum gas (LPG), or oil; and fishing vessels are more likely to be associated with human errors. Further, one important finding is that the involvement of fishing vessels significantly increases the occurrence probability of both negligence errors and judgment or operational errors. In addition, the occurrence likelihood of human errors is generally higher in springtime, conditions of poor visibility, the absence of strong winds or waves, and the moored or docked status.

Four decades of aviation visibility at Bhairahawa airport, gateway to Buddha's birthplace Lumbini, Nepal

Over four decades of visibility data at Bhairahawa airport (BWA), a recently upgraded international airport near Lumbini, a UNESCO heritage site in Nepal has been analyzed. In this study, we also investigated one of the important microclimatic behavior of fog i.e., onset and dispersal timings, and its implication on aviation. Temporal variations of poor visibility conditions at BWA are found to be primarily associated with variations in haze and fog. Haze at BWA accounts for the highest percentage (∼27%) of time and its annual occurrence is increasing (0.57% yr−1). There is a significant upward trend of hazy days in all seasons, the highest (1.46% day yr−1) being in post-monsoon. The overall seasonal poor visibility, too, has increased significantly in all seasons, with the highest trend in post-monsoon (1.57% yr−1). Similarly, fog frequency in the winter season has also increased noticeably for fog days (1.05% day yr−1), dense fog days (0.51% day yr−1), general fog hours (0.55% hour yr−1) and dense fog hours (0.20% hour yr−1). We found that fog at BWA is usually formed overnight and dissipates before noon. Daytime onset and late dispersion of fog are more common in the peak winter months of December and January. Further, we investigated the relationship between visibility and aerosol optical depth (AOD) and found a moderate negative correlation (r = − 0.66, p < 0.001) between them in the monsoon season. However, AOD is found to have a weaker correlation with visibility during winter (r = −0.36) and pre-monsoon (r = −0.23) seasons, when there is a more pronounced influence of meteorological conditions on the occurrence of visibility. We have observed a better correlation (r = −0.74) between fine particulate matter concentration (PM2.5) and visibility. Examining the effect of relative humidity (RH) on AOD (or, PM2.5) and visibility revealed that higher RH tends to lower visibility. Visibility at BWA airport is gradually worsening due to local and regional air pollution emissions and changing meteorological conditions. The degraded visibility at BWA airport will negatively impact flight safety and timeliness. Effective implementation of regionally coordinated air pollution mitigation measures can be a sustainable step towards the improvement of visibility in long run. However, the installation of ground equipment like CAT-II/III Instrument Landing System (ILS) for aircraft take-off and landings, and advanced surface movement guidance and control system (A-SMGCS) are highly advisable to lessen the damage potential to aviation.

Real-world low-light image enhancement via domain-gap aware framework and reverse domain-distance guided strategy

Low-light image enhancement (LLIE) has high practical value and development potential in real scenarios. However, the current LLIE methods reveal inferior generalization competence to real-world low-light (LL) conditions of poor visibility. We can attribute this phenomenon to the severe domain bias between the synthetic LL domain and the real-world LL domain. In this article, we put forward the Domain-Gap Aware Framework, a novel two-stage framework for real-world LLIE, which is the pioneering work to introduce domain adaptation into the LLIE. To be more specific, in the first stage, to eliminate the domain bias lying between the existing synthetic LL domain and the real-world LL domain, this work leverages the source domain images via adversarial training. By doing so, we can align the distribution of the synthetic LL domain to the real-world LL domain. In the second stage, we put forward the Reverse Domain-Distance Guided (RDDG) strategy, which takes full advantage of the domain-distance map obtained in the first stage and guides the network to be more attentive to the regions that are not compliance with the distribution of the real world. This strategy makes the network robust for input LL images, some areas of which may have large relative domain distances to the real world. Numerous experiments have demonstrated the efficacy and generalization capacity of the proposed method. We sincerely hope this analysis can boost the development of low-light domain research in different fields.

Stochastic simulation of a signal on a photodetector matrix of a laser navigation system

Abstract Algorithms for stochastic simulation of the signal arriving at the photodetector matrix of the aircraft navigation system are constructed. During the operation of the aircraft landing navigation system, the laser beam of the navigation system coincides in its direction with the glide path determining safe landing. Two photodetector units placed on board of the aircraft determine the glide path position in the coordinate system of the aircraft, which allows one to adjust the position of the touch point on the runway in poor visibility conditions. In this case, the estimation of the power and angular distributions of radiation recorded by the receiver of the navigation system is a relevant problem. In addition, it is interesting to study the effect of scattering of various multiplicities on the recorded signal. The algorithms developed here are based on direct statistical modelling (estimation over collisions) and local estimates of the Monte Carlo method. The calculations show that the proposed methods allow us to evaluate the efficiency of the laser navigation system in various conditions.

Visual odometry and mapping under poor visibility conditions using a stereo infrared thermal imaging system

In the past decades, developments in the field of computer vision have made both the software and hardware more capable and more easily accessible. This has enabled otherwise complex vision systems to be used in other fields, such as autonomous robotics. Although vision systems in the visible light spectrum are commonplace in robotics nowadays, the thermal spectrum is still rarely used, even though it offers certain advantages. A thermal camera can sense the temperature of objects, is independent of illumination and can actually see through heavy smoke and fog. This makes it a useful tool in particular in the field of rescue robotics, where poor vision conditions are to be expected. In this paper, the feasibility of using two thermal cameras in a stereo vision setup to map indoor scenes is to be examined. It is meant to allow an autonomous robot to perceive its indoor surroundings as a 3D space, even in poor vision conditions. The biggest challenges are the calibration of thermal cameras and the proper filtering of the raw image and the resulting disparity map. Simple and easily implemented solutions are proposed for each of these issues.

Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.