Poor Visibility Conditions Research Articles

Bayesian Regression Model for Estimating Economic Loss Resulting from Two-Ship Collisions

This study aims to develop a Bayesian regression model to evaluate the economic loss resulting from two-ship collisions using ten years’ ship collision data for occurrances in Fujian waters. The model results show that the involvement of passenger/cruise ships could cause the largest increment on economic loss in ship collisions. Interestingly, it is found the involvement of fishing ships could greatly increase the ship collision consequence in terms of economic loss. Results also reveal that the higher economic loss is associated with the collisions in the straits/sea areas, under the strong wind/wave conditions, during nighttime period, and in poor visibility conditions. The impact analysis results highlight that judgment errors play a decisive role in increasing the economic loss as compared with the other two types of human errors: lookout failure and operation errors. The results of this study are useful for policy-makers in proposing efficient strategies to mitigate the economic loss from two-ship collisions. The developed model is also beneficial for insurance companies in determining the appropriate ship insurance rates.

Range-Gated Imaging System for Underwater Monitoring in Ocean Environment

High-quality video observations are very much needed in underwater environments for the monitoring of several ecosystem indicators and to support the sustainable development and management of almost all activities in the ocean. Reliable video observations are however challenging to collect, because of the generally poor visibility conditions and the difficulties to deploy cost-effective sensors and platforms in the marine environment. Visibility in water is regulated by natural light availability at different depths, and by the presence of suspended particles, scattering incident light in all directions. Those elements are also largely variable in time and space, making it difficult to identify technological solutions that can be used in all conditions. By combining state-of-the-art “time of flight” (ToF) image sensors and innovative pulsed laser illumination, we have developed a range-gated camera system (UTOFIA) that enables affordable and enhanced 3D underwater imaging at high resolution. This range-gated solution allows users to eliminate close-range backscattering, improving quality of the images and providing information on the distance of each illuminated object, hence giving access to real-time 3D measurements. Furthermore, as the system is based on pulsed laser light, it is almost independent of natural light conditions and can achieve similar performances at an extended depth range. We use this system to collect observations in different oceanographic conditions and for different applications, including aquaculture monitoring, seafloor mapping, litter identifications and structure inspection. Performances are evaluated by comparing images to regular cameras and by using standard targets to assess accuracy and precision of distance measurements. We suggest that this type of technology can become a standard in underwater 3D imaging to support the future development of the ocean economy.

Research on shipborne aided navigation system based on enhanced traffic environment perception.

The traditional shipborne navigation system uses a two-dimensional electronic chart as a platform to integrate and control a variety of electronic navigation equipment information. It cannot intuitively restore the actual traffic environment or fundamentally improve the perception ability of the crew in poor visibility conditions. This paper proposes integrating three-dimensional simulations and real ship driving systems and presents research on “virtual–real” and “dynamic–static” ship navigation technology and equipment. In the proposed method, the hydrologic factors, topographic features, waterways, traffic dynamics, and ship driving information are effectively integrated, with a focus on achieving key advancements such as the integration of simulations and real ship driving as well as multi-source information fusion. A multi-angle, all-around, multi-level visual display for water traffic environments in poor visibility conditions is provided to enhance the ability of crews to perceive their traffic environments and thereby to provide auxiliary navigation for ships in complex weather conditions.

The comparison of visibility measurement between image-based visual range, human eye-based visual range, and meteorological optical range

Before the 21st century, visibility monitoring techniques are mainly categorized by human eye-based and optical-based measurements. In order to automate and objectify the former measurement, the latter measurement method has been developed and meteorological optical range (MOR) is used as a concept similar to the visual range. However, in the 21st century, MOR has been found to have some differences with visual range that is recognized by human beings. As image analysis technology is improved, a variety of image-based on-site visibility measurement techniques are being developed using a camera. In this study, image-based visibility measurement that is based on the measured distance to visible objects was performed at about 3 km northwest of the Seoul Meteorological Observatory where the human eye-based and the optical-based visibility monitoring are observed simultaneously. As a result of correlation analysis between three measurement data, the image-based visual range (IVR) was found to be more comparable to the human eye-based visual range (HVR) than to the optical-based MOR. HVR tended to be lower than IVR as visibility improved, but MOR was predominantly higher than IVR as visibility was reduced. Perceived visibility measurement of IVR and HVR were revealed to be more accurate in severe poor visibility conditions. In this study, the system uncertainty of IVR estimation is expected to be within 10% up to about 33 km and approach 20% under above a hundred km of visibility. The Visual Range Meter (VRM) program developed for IVR estimation was made up of an individual measurement mode and a real-time measurement mode. The operational errors of approximately 15% excluding precipitation and other meteorological effects occurred in real-time IVR estimation using the VRM.

Real-time computerized video enhancement for minimally invasive fetoscopic surgery

BackgroundThe only definitive treatment for twin-to-twin transfusion syndrome is minimally invasive fetoscopic surgery for the selective coagulation of placental blood vessels. Fetoscopic surgery is a technically challenging operation, mainly due to the poor visibility conditions in the uterine environment. We present the design of an algorithm for the computerized enhancement of fetoscopic video and show that the enhanced video increases the ability of human users to identify blood vessels within fetoscopic video rapidly and accurately. MethodsA computer algorithm for the enhancement of fetoscopic video frames was created. First, optical fiber artifacts were removed via a modification of unsharp masking. Second, image contrast was increased via Contrast Limited Adaptive Histogram Equalization (CLAHE). Third, the effect of contrast enhancements on stationary features was removed by normalizing to a windowed mean of the video frames. Fourth, color information was reincorporated by combining the mean-normalized result with the unnormalized contrast enhanced image using the soft light blending algorithm. Medical trainees (n = 16) were recruited into a study to validate the algorithm. Subjects were shown enhanced or unenhanced fetoscopic video frames on a screen and were asked to identify whether a randomly placed marker fell on a blood vessel or on background. The accuracy of their responses was recorded. ResultsOn the subset of images where subjects had the lowest mean accuracy in identifying the placement of the marker, subjects performed better when viewing video frames enhanced by the computer (accuracy 74.27%; SE 0.97) than when viewing unenhanced video frames (accuracy 63.78%; SE 2.79). This result was statistically significant (p < 0.01). ConclusionReal-time computerized enhancement of fetoscopic video has the potential to ease the readability of video in poor lighting conditions, thus providing a benefit to the surgeon intraoperatively.

Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports

This paper presents the results of statistical analyses of maritime accidents data and AIS data from Norwegian waters, to identify conditions that are associated with navigation related accidents (groundings and collisions) and could be used as risk indicators. Vessels involved in accidents reported in the accident database of the Norwegian Maritime Directorate (NMA), have been traced in historical AIS records, and data related to each ship have been transformed into variables. These variables are related to the behavior of the ship in front of the accident (e.g. nautical miles sailed, hours in operations, number of port calls etc.), technical and organizational conditions (ship categories, flag state, age, gross tonnage, Paris MoU ratings etc.) and the area where the accident occurred (number of vessels in the area, port calls in the area, nautical miles in the area etc.). Both the AIS data and the data from the NMA accident database have first been analyzed using correspondence analysis (categorical variables), F-tests (continuous variables), and then combined in a multivariate logistic regression model with “navigation accidents” and “other accidents” as dependent variables. The model is a strong predictor for whether the accident is navigation-related or not. Specifically, some vessel types, less vessel length, poor visibility condition, and a flag of convenience increased this probability.

Spatial memory enhances the evacuation efficiency of virtual pedestrians under poor visibility condition**Project supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Grant No. 11203615).

Spatial memory is a critical navigation support tool for disoriented evacuees during evacuation under adverse environmental conditions such as dark or smoky conditions. Owing to the complexity of memory, it is challenging to understand the effect of spatial memory on pedestrian evacuation quantitatively. In this study, we propose a simple method to quantitatively represent the evacueeʼs spatial memory about the emergency exit, model the evacuation of pedestrians under the guidance of the spatial memory, and investigate the effect of the evacueeʼs spatial memory on the evacuation from theoretical and physical perspectives. The result shows that (i) a good memory can significantly assist the evacuation of pedestrians under poor visibility conditions, and the evacuation can always succeed when the degree of the memory exceeds a threshold (); (ii) the effect of memory is superior to that of “follow-the-crowd” under the same environmental conditions; (iii) in the case of multiple exits, the difference in the degree of the memory between evacuees has a significant effect (the greater the difference, the faster the evacuation) for the evacuation under poor visibility conditions. Our study provides a new quantitative insight into the effect of spatial memory on crowd evacuation under poor visibility conditions.

AUV homing and docking for remote operations

One of the major goals of the SUNRISE FP7 project is to make the Underwater Internet of Things a reality. In this context, the LOON-DOCK project presented here extends the existing Litoral Ocean Observatory Network testbed with a Docking Station tailored to the Sparus II AUV. The docking system allows a remote user to program survey-like missions through a web-based interface as well as to retrieve the data gathered by the AUV once a mission finalizes. To enable the autonomous docking of the AUV, two complementary and cost-effective localization systems have been developed. The first one implements a range-only localization algorithm to approach the docking station while the second, based on active light beacons, provides high accuracy at short ranges to complete the docking maneuver. The system has been extensively tested, in different trials from a controlled water tank environment to more realistic sea operation conditions proving its viability despite very poor water visibility conditions.

Flight demonstration of spaceborne real-time angles-only navigation to a noncooperative target in low earth orbit

The paper describes the onboard vision-based navigation software employed during the DLR's AVANTI experiment and presents its flight performance. The navigation task relies on a dedicated target detection module in charge of extracting the line-of-sight observations from the images taken by a monocular camera. The recognition of the target is based on the kinematic identification of its trajectory over a sequence of images. Subsequently, the resulting angles-only measurements are validated dynamically and processed by an Extended Kalman Filter to derive in real-time the relative state estimate. A computationally light implementation of the filter is achieved by the use of an analytical relative motion model. Two autonomous rendezvous to a noncooperative object have been performed in orbit, first from 13 km to 1 km separation, then from 3 km to 50 m. Despite the poor visibility conditions and the strong orbit perturbations encountered at low altitude, the filter was able to supply the onboard guidance and control algorithms with accurate and reliable relative state estimation, enabling thus a safe and smooth approach.

Degradation of Retro-Reflectivity of Thermoplastic Pavement Markings: A Review

For an effective thermoplastic pavement marking replacement strategy, the rate at which the marking’s retro-reflectivity deteriorates in service must be well established in order to avoid re-stripping that is too soon or too late. Against this background, this paper undertook a review of models that deal with degradation of thermoplastic pavement markings under different traffic and environmental conditions in order to establish service life and the terminal retro-reflectivity levels that have informed re-striping. Service life in the context of this paper is the time taken for a newly-installed marking to degrade to some minimum retro-reflectivity level below which motorists would find it difficult to navigate on the carriageway under night-time and poor visibility conditions. It was established that the minimum retro-reflectivity requiring re-stripping intervention reported varied, although commonly-adopted values tended to range from 50 mcd/m2/lx to 150 mcd/m2/lx. A number of empirical models, based on site specific conditions, have been developed by researchers using field data, to estimate marking retro-reflectivity at any time since placement. Whereas some of the models used time as the only independent variable, others used a combination of time, traffic level and a few other parameters to estimate retro-reflectivity. Even though degradation of marking retro-reflectivity is a reflection, among other things of material degeneration impacted by environmental and service conditions, almost all the models reviewed failed to consider environmental factors. Additionally, for some of the models, non-inclusion of the initial retro-reflectivity level and their generally low coefficient of determination statistic erode the confidence in their reliability.

An Enhanced Multi-GNSS Navigation Algorithm by Utilising a Priori Inter-System Biases

The integration of multi-constellation Global Navigation Satellite System (GNSS) measurements can effectively improve the accuracy and reliability of navigation and positioning solutions, while the Inter-System Bias (ISB) is a key issue for compatibility. The ISB is traditionally estimated as an unknown parameter along with three-dimensional position coordinates and a receiver clock offset with respect to Global Positioning System (GPS) time. ISB estimation of this sort will sacrifice a satellite observation for each integrated GNSS system. These sacrificed observations could be vital in situations of limited satellite visibility. In this study, an enhanced multi-GNSS navigation algorithm is developed to avoid sacrificing observations under poor visibility conditions. The main idea of this algorithm is to employ a moving average filter to smooth the ISBs estimated at previous epochs. The filtered value is utilised as a priori information at the current epoch. Experimental tests were conducted to evaluate the enhanced algorithm under open and blocked sky conditions. The results show that the enhanced algorithm effectively improves the accuracy and availability of navigation solutions under the blocked sky condition, with performance being comparable to traditional ISB estimation algorithms in open sky conditions. The improvement rates of the three-dimensional position accuracy and availability reach up to 63% and 21% in the blocked sky environment. Even in the case of only four different GNSS satellites, a position solution can still be obtained using the enhanced algorithm.

Accessibility evaluations for nighttime walking and bicycling for low-income shift workers

This paper addresses an important issue related to nighttime commuting of low-income shift workers who walk and/or bike to their workplace using public transit. A shift worker is anyone who follows a work schedule that is outside of the typical daytime working hours of a business day and commute after dark - by walking or bicycling to a transit stop. However, poor visibility conditions on sidewalks and bicycle lanes often thwart safety of their walking and bicycling activities. Therefore, this paper develops two simple scores - nighttime accessibility score for walking (NASW) and nighttime accessibility for bicycling (NASB) - for evaluating nighttime infrastructure for pedestrians and bicyclist. The scores consider the employment data, travel time and the physical distribution of streetlight poles along the sidewalks and bicycle lanes. Data from the city of El Paso in Texas is used to demonstrate the applicability of the two scores. Employment data from three prominent service industry sectors known to employ low-income shift workers – i) Retail Trade, ii) Accommodation and Food Services, and iii) Health Care and Social Assistance – are used for demonstration purposes. It is observed that amongst the three sectors analyzed, both NASW and NASB values are higher for regions in El Paso with low-income employment concentrations from the Health Care and Social Assistance sector. It is also observed that some prominent regions in north-east, south-east and west of El Paso need improvements in streetlight systems, sidewalks and bicycle network to facilitate walking and bicycling amongst low-income shift workers employed in Retail Trade, and Accommodation and Food Services industry sectors to access transit stops at night.

WiSafe: Wi-Fi Pedestrian Collision Avoidance System

In this paper, we present a novel Wi-Fi-based pedestrian collision avoidance (PCA) system: WiSafe. Compared to vision-based and radar-based PCA systems, WiSafe has a key advantage of Non-Line-of-Sight (NLOS). WiSafe can assist drivers in discovering pedestrians in NLOS and blind spots, where the views of drivers are blocked by buildings, vehicles, or other obstacles. In particular, it is very useful when driving in poor visibility conditions such as at night, in heavy rain, or thick fog. WiSafe can save split seconds, which can potentially save lives. Instead of using wireless access in vehicular environments/dedicated short-range communications to protect pedestrians, WiSafe provides a simple, effective, and low-cost solution. Without changing Wi-Fi standards, WiSafe is compatible with 802.11a/b/g/n and can be implemented by apps on smartphones or wearable devices. The complete analysis, implementation, and experiments are presented in this paper. The results show that WiSafe can achieve and even exceed the PCA requirements.

Selected Aspects of the Low Level Automatic Taxi Control System Concept

Abstract Taxiing of manned and remotely piloted aircraft is still performed by pilots without using a system of automatic control of direction and speed. Several reasons have emerged in recent years that make the automation of taxiing an important design challenge. The reasons are: decreased airport capacity due to the growing number of aircraft, poor ground operation conditions during poor visibility conditions, an increase in workload of pilots and air traffic controllers and the integration of simultaneous ground operations of manned and remotely piloted air vehicles. This paper presents selected aspects of the concept of a Low Level Automatic Taxi Control System. In particular, it emphasizes the means of controlling an aircraft during taxiing, accuracy requirements of the system and proposes control techniques. The resulting controller of the system is adaptable for different aircrafts. The actuators and their mechanical connections to available controls are the aircraft specific part and are designed for the particular type – in this case – a general aviation light airplane.

AN ALGORITHM FOR PEDESTRIAN DETECTION IN MULTISPECTRAL IMAGE SEQUENCES

Abstract. The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

Association between intraabdominal pressure during gynaecologic laparoscopy and postoperative pain.

Laparoscopy is nowadays a well-established surgical method and plays a main role in an ever-increasing range of indications in gynaecology. High-quality studies of surgical techniques are necessary to improve the quality of patient care. The present study aims at evaluating postoperative pain after gynaecological laparoscopy depending on the intraoperative CO2 pressure. In a prospective, monocentric, randomized single-blind study at the Department of Gynaecology and Obstetrics at the Hannover Medical School, we include patients scheduled for different laparoscopic procedures. Randomization of the intraoperative CO2 pressure was carried out in six groups. Pain was assessed the day after surgery by the blinded nurse using a visual analogue scale. 550 patients were included in the period from May 2013 to January 2016. The analysis of the per protocol population PPP (n = 360) showed no statistically significant difference between the six intervention groups with regard to mean postoperative pain perception. In direct comparison between two groups, an intraoperative CO2 pressure of 15mmHg was associated with a significant higher pain score than a pressure of 12mmHg. The difference was 7.46mm on a 10cm VAS. The results of our study indicate that a CO2 pressure of 12 versus 15mmHg can be advantageous. However, the clinical relevance remains unclear due to the low difference in pain. The additional benefit of an even lower pressure of 10 or 8mmHg cannot be reliably assessed; we found signs of poor visibility conditions in these low pressure groups.

Improved Situation Awareness for Autonomous Taxiing Through Self-Learning

As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs' autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then, we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. An indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically.

Colour quantitation for chemical spot tests for a controlled substances presumptive test database.

Crime scene investigators (CSIs) often encounter unknown powders, capsules, tablets, and liquids at crime scenes, many of which are controlled substances. Because most drugs are white powders, however, visual determination of the chemical identity is difficult. Colourimetric tests are a well-established method of presumptive drug identification. Positive tests are often reported differently, however, because two analysts may perceive colour or record colourimetric results in different ways. In addition to perceiving colour differently, it is very common for there to be poor visibility conditions (e.g. rain, darkness) while performing these tests, further obscuring the results. In order to address these concerns and to create uniformity in the reporting of on-site colourimetric test results, this study has evaluated two of the state-of-the-art apps (ColorAssist® and Colorimeter®) for reporting the colour test results quantitatively in red-green-blue (RGB) format. The compiled library database of presumptive test results contains over 3300 data points including over 800 unique drug/test combinations. Variations observed between test replicates, from performing a test on different days, recording with a different device type (e.g. iPod Touch, iPhone models 4, 5c, 5s, or 6), and using different quantities of drug are discussed. Overall, the least variation in Euclidian norm was observed using ColorAssist® with the camera light (25.1±22.1) while the variation between replicates and data recorded using different devices was similar. The resulting library is uploaded to a smartphone application aimed to aid in identifying and interpreting suspected controlled substance evidence. Copyright © 2016 John Wiley & Sons, Ltd.

Vision-Based Autonomous Underwater Vehicle Navigation in Poor Visibility Conditions Using a Model-Free Robust Control

This paper presents a vision-based navigation system for an autonomous underwater vehicle in semistructured environments with poor visibility. In terrestrial and aerial applications, the use of visual systems mounted in robotic platforms as a control sensor feedback is commonplace. However, robotic vision-based tasks for underwater applications are still not widely considered as the images captured in this type of environments tend to be blurred and/or color depleted. To tackle this problem, we have adapted thelαβcolor space to identify features of interest in underwater images even in extreme visibility conditions. To guarantee the stability of the vehicle at all times, a model-free robust control is used. We have validated the performance of our visual navigation system in real environments showing the feasibility of our approach.

Robotic Visual Tracking of Relevant Cues in Underwater Environments with Poor Visibility Conditions

Using visual sensors for detecting regions of interest in underwater environments is fundamental for many robotic applications. Particularly, for an autonomous exploration task, an underwater vehicle must be guided towards features that are of interest. If the relevant features can be seen from the distance, then smooth control movements of the vehicle are feasible in order to position itself close enough with the final goal of gathering visual quality images. However, it is a challenging task for a robotic system to achieve stable tracking of the same regions since marine environments are unstructured and highly dynamic and usually have poor visibility. In this paper, a framework that robustly detects and tracks regions of interest in real time is presented. We use the chromatic channels of a perceptual uniform color space to detect relevant regions and adapt a visual attention scheme to underwater scenes. For the tracking, we associate with each relevant point superpixel descriptors which are invariant to changes in illumination and shape. The field experiment results have demonstrated that our approach is robust when tested on different visibility conditions and depths in underwater explorations.