Low Visibility Environments Research Articles

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance

AbstractMaritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.

Evaluation of Detection Performance for Safety-Related Sensors in Low-Visibility Environments

This paper addresses the evaluation of object detection performance for non-contact safety-related sensors in low-visibility environments due to adverse weather conditions. An evaluation metric, MOT (Minimum Object-detectable Transmittance), is proposed to quantitatively evaluate the influence of the low-visibility environments on the object detection performance. Traditionally, the Meteorological Optical Range (MOR) has been used for this purpose. However, MOR is not appropriate for the sensor evaluation because it is a meteorological metric which is optimized for human eyes to estimate the distance at which a person can see clearly through an atmosphere. On the other hand, MOT is a physical and optical measure that quantifies the relationship between the spatial transmittance and the distance at which a sensor can detect a specified object. It is specialized for the sensor evaluation and can express the sensor characteristics in the low-visibility environments at each measurement distance. The usefulness of MOT will be demonstrated by presenting the experimental results of MOT measurements for various sensor devices in a fog space reproduced with environmental simulator equipment.

Speed Guidance and Trajectory Optimization of Traffic Flow in a Low-Visibility Zone of a Highway Segment within Multiple Signalized Intersections

In a low-visibility environment, the speed of traffic flow is more discrete on highways, which easily causes traffic accidents. To improve the safety and efficiency level of low-visibility zones on highways, a comprehensive optimization control method is proposed. The method uses formations of a low-visibility zone to queue discrete vehicles from the upstream intersection. Then, according to the relationship between the arrival time of the fleet and the phase state of the intersection, speed guidance and trajectory optimization are carried out to improve the traffic efficiency of the fleet that enters the low-visibility zone. Finally, a low-visibility zone of a highway segment is analysed with different traffic flows for the main road and the secondary roads, and the results show that the proposed optimization control method can prevent trajectory overlap and effectively decrease traffic delays on highways.

First Step toward Gestural Recognition in Harsh Environments.

We are witnessing a rise in the use of ground and aerial robots in first response missions. These robots provide novel opportunities to support first responders and lower the risk to people’s lives. As these robots become increasingly autonomous, researchers are seeking ways to enable natural communication strategies between robots and first responders, such as using gestural interaction. First response work often takes place in harsh environments, which hold unique challenges for gesture sensing and recognition, including in low-visibility environments, making the gestural interaction non-trivial. As such, an adequate choice of sensors and algorithms needs to be made to support gestural recognition in harsh environments. In this work, we compare the performances of three common types of remote sensors, namely RGB, depth, and thermal cameras, using various algorithms, in simulated harsh environments. Our results show 90 to 96% recognition accuracy (respectively with or without smoke) with the use of protective equipment. This work provides future researchers with clear data points to support them in their choice of sensors and algorithms for gestural interaction with robots in harsh environments.

Reasonable driving speed limits based on recognition time in a dynamic low-visibility environment related to fog—A driving simulator study

To determine a reasonable speed limit and ensure traffic safety in a dynamic low-visibility environment with fog, a driving simulator study was conducted. A total of 31 young participants were recruited, and each completed 5 driving simulator trials under varying visibility conditions and speed levels during the daytime. The combined coupling effect of the visibility and driving speed on drivers’ recognition times was explored, and a quantitative model of the recognition time, visibility, and driving speed was established. A determination method and suggested value of a reasonable driving speed limit in dynamic low-visibility conditions were proposed based on the stopping sight distance model. The results show that there are significant differences in the recognition times of drivers under different visibility and speed conditions. The reasonable driving speed limit values in dynamic low-visibility conditions should be based on visibility changes. When the stopping sight distance is 75 m and the visibility is less than 35 m, the speed limit should be 20 km/h. When the visibility is between 35 m and 60 m, the speed limit should be 30 km/h. When the visibility is between 60 m and 140 m, the speed limit should be 50 km/h. When the visibility is greater than 140 m, the speed limit should be 60 km/h. These research results can provide a theoretical reference for the formulation of a VSL in a dynamic low-visibility environment related to fog and reduce crash risk in conditions of inadequate visibility in fog.

Multiple scattering and modeling of laser in fog* *Project supported by the National Natural Science Foundation of China (Grant Nos. 61571355 and 61401342).

When a laser is transmitted in fog, and the water droplets will scatter and absorb the laser, which affects the intensity of the laser transmission and the accuracy of radar detection. Therefore, it is of great significance to study the laser transmission in the fog. At present, the main method of calculating the scattering and attenuation characteristics of fog is based on the radiation transmission theory, which is realized by a large number of numerical calculations or physical simulation methods, which takes time and cannot meet the requirements for obtaining the fast and accurate results. Therefore, in this paper established are a new laser forward attenuation model and backward attenuation model in low visibility fog. It is found that in low visibility environments, the results calculated by the Monte Carlo method are more accurate than those from most of the existing forward attenuation models. For the cases of 0.86-μm, 1.06-μm, 1.315-μm, 10.6-μm typical lasers incident on different fogs with different visibilities, a backscatter model is established, the error between the fitting result and the calculation result is analyzed, the backward attenuation fitting parameters of the new model are tested, and a more accurate fitting result is obtained.

A BIM-Based Coordination Support System for Emergency Response

In fire emergencies, timely communication with on-site coordinators and accurate localization of first responders facilitates effective task assignment and resource allocation in harsh, low-visibility environments. Building information modeling (BIM) is widely accepted in the architecture, engineering, and construction industries as a central repository of building information. It could provide both the geometric building data and semantic information; however, the convenient linkage and integration with indoor location technologies for emergency purposes have not been addressed according to the authors’ knowledge. A stand-alone BIM-based indoor location (BIMIL) framework and portal were designed and tested to enable the automatic extraction, transformation, and visualization of BIM-related data for public safety purposes in this study. Based on current information technology, this research reduces the gap in cross-application by supporting indoor location to overcome the primary shortcoming of existing indoor building models. Eliminating the need for specific software and skill in data processing, this portal will support on-site coordinators’ importation of BIM files, allowing them to convert those files into processed and visualized indoor information containing key yet simplified geometric building data and essential emergency-related information such as fire rating hours, egresses, and hazardous materials. Additionally, the indoor location data can be integrated into a generalized 3D building model to support decision-making activities and management tasks in the field.

Cross Fusion-Based Low Dynamic and Saturated Image Enhancement for Infrared Search and Tracking Systems

Unmanned aerial vehicles and battleships are equipped with the infrared search and tracking (IRST) systems for its mission to search and detect targets even in low visibility environments. However, infrared sensors are easily affected by diverse types of conditions, therefore most of IRST systems need to apply advanced contrast enhancement (CE) methods to cope with the low dynamic range of sensor output and image saturation. The general histogram equalization for infrared images has unwanted side effects such as low contrast expansion and saturation. Also, the local area processing for saturation reduction has been studied to solve the problems regarding the saturation and non-uniformity. We propose the cross fusion based adaptive contrast enhancement with three counter non-uniformity methods. We evaluate the proposed method and compare it with conventional CE methods using the discrete entropy, PSNR, SSIM, RMSE, and computation time indexes. We present the experimental results for images from various products using several datasets such as infrared, multi-spectral satellite, surveillance, general gray and color images, as well as video sequences. The results are compared using the integrated image quality measurement index and they show that the proposed method maintains its performance on various degraded datasets.

Analysis of High-Accuracy Satellite Messages for Road Applications

Future road applications will require positioning accuracy on the order of one or a few decimeters and a convergence time measured in seconds. A high-accuracy service (HAS) provided by the Global Navigation Satellite System becomes a key enabler to meet these requirements. In particular, the Galileo program has committed to provide a free HAS. This entails several design challenges; namely, to achieve the necessary performance for all users, high-accuracy messages must be optimally formatted, encoded, and broadcast. This article presents an overview of satellite-based HASs and evaluates different transmission schemes based on field and laboratory results, proposing some recommendations for the implementation of an HAS. The article concludes that satellite-based, high-accuracy messages, such as Galileo's, can provide fast convergence and sufficient accuracy worldwide, even in low-visibility environments.

Stage-specific impacts of hazy weather on car following

The impacts of low visibility due to hazy weather (HW) conditions on longitudinal car-following (CF) driving behaviour for three different stages (accelerating, decelerating and steady CF stages) were investigated. Clear weather and HW scenarios simulated by a high-fidelity driving simulator were used in the investigation. The simulated results showed that low-visibility environments due to HW conditions have substantial and stage-specific impacts on driving behaviour. For instance, in HW conditions, drivers’ sensitivity to changes in relative speed decreases in the accelerating CF stage but increases in the decelerating and steady CF stages. The results indicate that, in HW conditions, drivers adopt earlier acceleration actions to follow the lead vehicle closely and take quicker and sharper braking actions to avoid collisions due to the smaller headway. The results provide useful references for developing microscopic traffic simulations and traffic strategies in HW conditions.

Research on active polarization imaging experiments and key technologies in smoke and dust environment

For realizing the engineering application of key technologies in the long-range active polarization imaging system, an experimental platform is built to study the static active polarization imaging technology and related image fusion algorithm in the smoke and dust environment. In the experiments, 532 nm CW laser is used as illumination source after collimation and beam expansion, metal bullet model is used as detection target, smoke environment is created through smoke generator in organic glass hood, polarization direction can be rotated by the polarizer, the images are collected by Lumerera-LM135 camera. The analysis and handling on the images are conducted by the use of the MATLAB software. In the aspects of image information entropy, average gradient, spatial frequency and standard deviation, the polarization imaging effects in various environments are quantitatively compared and analyzed, and the imaging advantages of laser illumination and polarization imaging technology in smoke and dust environment and dim conditions are verified. In order to further improve the imaging quality of active polarization imaging technology, a polarization image fusion algorithm based on fuzzy adaptive is proposed after analyzing a large number of data acquired in the experiment.The experimental results show that the active polarization imaging technology and the improved image fusion algorithm can improve the detection distance of optical system and image contrast, expand the digestible information of images for the long-range dim target imaging in low visibility environment, and provide important data reference and technology support for the application of this technology in shooting range.

Lack of light colour effects when sampling fish at night in low visibility environments.

We compared night deployments of Baited Remote Underwater Video Stations (BRUVS) in South Australia, illuminated by either red or blue light and found similar species compositions and abundances with both colours. We observed c. 1800 individuals from 52 species across 107 night-time deployments, with only one site out of six showing differences in assemblages between the two colours. With five out of six sites showing similarities between light colours our results differ from previous results and more generally suggest that the most suitable colour can differ among studies.

多特征级联的低能见度环境红外行人检测方法

多特征级联的低能见度环境红外行人检测方法

A novel variable baseline visibility detection system and its measurement method

As an important meteorological observation instrument, the visibility meter can ensure the safety of traffic operation. However, due to the optical system contamination as well as sample error, the accuracy and stability of the equipment are difficult to meet the requirement in the low-visibility environment. To settle this matter, a novel measurement equipment was designed based upon multiple baseline, which essentially acts as an atmospheric transmission meter with movable optical receiver, applying weighted least square method to process signal. Theoretical analysis and experiments in real atmosphere environment support this technique.

To see or not to see: Investigating the links between patient visibility and potential moderators affecting the patient experience

Perhaps more than ever before, healthcare organizations and designers strive to create patient environments that deliver a superlative patient experience. Outstanding healthcare design requires a delicate balance of interdependent characteristics. This exploratory investigation evaluated whether the staff-to-patient visibility (low versus high) is associated with potential moderating variables that may either enhance or detract from the patient experience. The findings from this investigation demonstrate that patient visibility is associated with improvements among three moderating variables, including increased staff presence in patient rooms, better staff perceptions of their work environment, and reduced unwanted noise reaching patients. The fourth moderator, privacy, was assumed to be higher in the low visibility condition. Although the data related to the patient experience did not allow statistical analysis of patient satisfaction scores, there was a three percent improvement in patient satisfaction in the low visibility environment (specifically overall rating). Although there was an increase in patient fall rates, this difference was not significant.

Head-Mounted Sensory Augmentation Device: Designing a Tactile Language.

Sensory augmentation operates by synthesizing new information then displaying it through an existing sensory channel and can be used to help people with impaired sensing or to assist in tasks where sensory information is limited or sparse, for example, when navigating in a low visibility environment. This paper presents the design of a 2nd generation head-mounted vibrotactile interface as a sensory augmentation prototype designed to present navigation commands that are intuitive, informative, and minimize information overload. We describe an experiment in a structured environment in which the user navigates along a virtual wall whilst the position and orientation of the user's head is tracked in real time by a motion capture system. Navigation commands in the form of vibrotactile feedback are presented according to the user's distance from the virtual wall and their head orientation. We test the four possible combinations of two command presentation modes (continuous, discrete) and two command types (recurring, single). We evaluated the effectiveness of this 'tactile language' according to the users' walking speed and the smoothness of their trajectory parallel to the virtual wall. Results showed that recurring continuous commands allowed users to navigate with lowest route deviation and highest walking speed. In addition, subjects preferred recurring continuous commands over other commands.

Autonomous Fire Suppression System for Use in High and Low Visibility Environments by Visual Servoing

An autonomous fire suppression system was developed for localized fire suppression in high and low visibility environments. The system contains a multispectral sensor suite, including UV sensors and infrared stereovision, to detect and target a fire for suppression. The UV sensor provides an alert to the system to begin fire detection. IR imagery is used to segment fire from the field of view and target the base of the fire and IR stereovision to determine the 3D coordinates of the fire. IR tracking provides continuously updated information on the size and intensity of the fire before and during suppression and alerts the system when to cease suppression activity. Visual servoing is used to correctly position a nozzle based on feedback of changes in the fire location and size. The autonomous system was used to suppress wood crib fires (40 kW to 50 kW) in high and low visibility environments and at varying distances (2.8 m to 5.5 m) and elevations (0.4 m to 1.3 m). The suppression time in clear conditions was 3.72 s ± 1.51 s and 4.49 s ± 1.62 s in low visibility conditions. To simulate wind effects and inaccurate initial target coordinates, forced offsets were input to the system to show effectiveness of the feedback control algorithm when an initial estimate of spray trajectory does not accurately spray the center base of the fire. System performance with a forced offset resulted in suppression times of 4.11 s ± 0.84 s.

Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins.

This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.

A kind of infrared expand depth of field vision sensor in low-visibility road condition for safety-driving

Purpose – This study aims to consider active vision in low-visibility environments to reveal the factors of optical properties which affect visibility and to explore a method of obtaining different depths of fields by multimode imaging.Bad weather affects the driver’s visual range tremendously and thus has a serious impact on transport safety. Design/methodology/approach – A new mechanism and a core algorithm for obtaining an excellent large field-depth image which can be used to aid safe driving is designed and implemented. In this mechanism, atmospheric extinction principle and field expansion system are researched as the basis, followed by image registration and fusion algorithm for the Infrared Extended Depth of Field (IR-EDOF) sensor. Findings – The experimental results show that the idea we propose can work well to expand the field depth in a low-visibility road environment as a new aided safety-driving sensor. Originality/value – The paper presents a new kind of active optical extension, as well as enhanced driving aids, which is an effective solution to the problem of weakening of visual ability. It is a practical engineering sensor scheme for safety driving in low-visibility road environments.