Low Visibility Environments Research Articles

Acoustic camera-based super-resolution reconstruction approach for underwater perception in low-visibility marine environments

In low-visibility environments, the underwater perception range of optical cameras is severely restricted, and perception operations in ocean engineering often rely on sonar. Acoustic cameras are a type of forward-looking sonar that have attracted considerable attention because of their ability to produce images similar to those of optical cameras. However, owing to the unique imaging mechanism employed by acoustic cameras, the resulting images suffer from insufficient resolution and a loss of feature details. This issue considerably diminishes the precision of downstream visual tasks, limiting the application of acoustic cameras. In this study, we propose a deep-learning-based super-resolution reconstruction approach for acoustic cameras, where the reconstruction process relies only on images, without prior assumptions regarding the detection scenes. We verified the effectiveness of the proposed method for two practical applications: marine debris detection and marine structure inspection. The experimental results show that our proposed method can robustly reconstruct high-resolution sonar images, and the obtained images have superior feature details, which improved the precision of downstream vision tasks. In this study, we aim to provide better solutions for underwater perception in low-visibility marine environments, while exploring the application of acoustic cameras in marine debris detection and structure inspection.

Enhanced Monitoring of Subsea Pipelines Using LiDAR for Corrosion and Structural Deformation Detection

The continuous and reliable operation of subsea pipelines is of critical importance to the global oil and gas industry, as these pipelines are responsible for transporting vast quantities of hydrocarbons from offshore production fields to onshore facilities. However, the harsh underwater environment in which these pipelines operate presents numerous challenges, including exposure to corrosive seawater, extreme pressure variations, and mechanical stress from geological movements. These factors can cause significant structural degradation over time, leading to potential corrosion, material fatigue, and deformation. Without regular monitoring, such issues may go undetected, increasing the risk of leaks, spills, or catastrophic failures, which would have severe environmental, economic, and safety consequences. Traditionally, subsea pipeline monitoring has relied on periodic inspections using technologies such as remotely operated vehicles (ROVs), sonar, and visual assessments. While these methods have proven useful, they often suffer from limitations such as low resolution, infrequent data collection, and the high operational costs associated with deploying ROVs and divers for manual inspections. Moreover, these traditional methods may not be capable of providing the continuous, real-time data needed to make proactive maintenance decisions and ensure pipeline integrity. This research paper focuses on the application of LiDAR (Light Detection and Ranging) technology as a transformative solution for monitoring the structural health of subsea pipelines. LiDAR is a remote sensing technology that uses laser pulses to capture detailed, high-resolution 3D data about surfaces and environments. By adapting LiDAR for underwater use, this paper proposes a novel method for real-time, continuous monitoring of pipeline conditions. The use of LiDAR in subsea environments presents unique opportunities to detect early signs of corrosion, material degradation, and structural deformation with a level of precision that is difficult to achieve with conventional monitoring techniques. The key advantage of LiDAR technology lies in its ability to collect accurate and comprehensive data in challenging conditions. Underwater LiDAR systems can penetrate turbid water, providing clear images and measurements of pipeline surfaces, even in low-visibility environments. The resulting high-resolution 3D models allow for the detailed analysis of pipeline integrity, enabling operators to track changes in pipeline conditions over time and respond to potential issues before they escalate. This capability is particularly important for detecting corrosion and deformation, which can occur gradually and may not be immediately apparent using traditional methods.In this paper, we explore the current state of subsea pipeline monitoring technologies, comparing the strengths and limitations of existing approaches. We then introduce a LiDAR-based solution, discussing the technical adaptations necessary for using LiDAR in subsea conditions. This includes considerations for data acquisition, processing, and analysis, as well as the challenges posed by underwater environments, such as light absorption and scattering by water particles. Furthermore, mathematical models for calculating corrosion rates and predicting deformation based on LiDAR data will be presented. These models allow for quantitative assessments of the structural health of pipelines, enabling operators to make informed decisions about maintenance schedules and interventions. The use of real-time data not only improves the accuracy of these models but also provides the ability to forecast future pipeline behavior, reducing the likelihood of unexpected failures. This paper also includes case studies and diagrams to demonstrate the practical application of LiDAR in subsea pipeline monitoring. By presenting real-world examples of how this technology has been implemented, we aim to highlight its effectiveness in improving the safety and longevity of critical infrastructure in the oil and gas sector. LiDAR technology represents a significant advancement in the field of subsea pipeline monitoring, offering enhanced data accuracy, real-time monitoring capabilities, and cost-effective solutions for ensuring pipeline integrity. This research paper not only outlines the potential of LiDAR as a monitoring tool but also provides a roadmap for its implementation in the oil and gas industry, with the ultimate goal of reducing risks, minimizing environmental impacts, and ensuring the continued safe operation of subsea pipelines.

AI monitoring and warning system for low visibility of freeways using variable weight combination model

AbstractIn intelligent vehicles, road environment perception technology is a key component of autonomous driving assistance systems. This component is the foundation for vehicle decision‐making and control, and is a guarantee of safety during the driving of the vehicle. The existing environment perception technology mainly targets well‐lit environments and requires visible light imaging equipment. Therefore, in low visibility environments, this technology cannot make good judgments about the external environment. Many existing perception systems mainly rely on sensors. Under low visibility conditions, these sensors weaken their effectiveness due to signal transmission, reflection, or absorption, resulting in incomplete or distorted data collection. Reduced visibility often affects the sensing range of various sensors, hindering the system's ability to detect and recognize distant objects, thereby limiting the necessary advance warning and response time for safe navigation. In response to this issue, this study proposed a combined method of infrared imaging and polarized imaging to collect feature data on road conditions in low visibility environments. Then, the obtained images were denoised and enhanced. The processed images were input into the system for recognition, and the images were analyzed and recognized using a low visibility road situation semantic segmentation algorithm based on deep learning. The research outcomes denoted that the pixel accuracy, average pixel accuracy, and average intersection ratio of the variable weight combination model in polarized degree images were 91.2%, 89.1%, and 71.6%, respectively. Those in infrared images were 83.6%, 90.6%, and 62.1%, respectively. The various indicators of the variable weight combination model were higher than those of the U‐shaped neural network model, indicating its performance is relatively excellent. The research results indicated that infrared imaging helps to acquire information at night or in low light conditions, while polarized imaging can provide better adaptation to cluttered light and reflections, enabling the system to provide more robust environmental sensing in complex weather conditions. It fills a critical gap in perception for autonomous driving systems in adverse weather conditions.

The Impact of Postures and Moving Directions in Fire Evacuation in a Low-Visibility Environment.

Walking speed is a significant aspect of evacuation efficiency, and this speed varies during fire emergencies due to individual physical abilities. However, in evacuations, it is not always possible to keep an upright posture, hence atypical postures, such as stoop walking or crawling, may be required for survival. In this study, a novel 3D passive vision-aided inertial system (3D PVINS) for indoor positioning was used to track the movement of 20 volunteers during an evacuation in a low visibility environment. Participants' walking speeds using trunk flexion, trunk-knee flexion, and upright postures were measured. The investigations were carried out under emergency and non-emergency scenarios in vertical and horizontal directions, respectively. Results show that different moving directions led to a roughly 43.90% speed reduction, while posture accounted for over 17%. Gender, one of the key categories in evacuation models, accounted for less than 10% of the differences in speed. The speeds of participants under emergency scenarios when compared to non-emergency scenarios was also found to increase by 53.92-60% when moving in the horizontal direction, and by about 48.28-50% when moving in the vertical direction and descending downstairs. Our results also support the social force theory of the warming-up period, as well as the effect of panic on the facilitating occupants' moving speed.

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog—a driving simulator study

Objective Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment. Methods A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed. Results The results show that reasonably selecting colored pavement can effectively improve drivers’ behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety. Conclusions The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Applications and analytical approaches using imaging sonar for quantifying behavioural interactions among aquatic organisms and their environment

Abstract For many aquatic animals, distributional patterns, trophodynamic relationships, and reproductive activity are driven by complex biotic and abiotic ecosystem interactions that influence behaviour. Linking behavioural information to environmental stimuli and stressors can, therefore, help to anticipate population and ecosystem responses to changing conditions and inform management. However, behavioural information is challenging to obtain because many sampling gears do not provide adequate spatial or temporal resolution, or potentially alter behaviours. Traditionally, most behavioural studies have been laboratory experiments, while behaviour in situ has often been inferred indirectly. Advancements in imaging sonar technology enable the study of in situ behaviours with the potential to address many understudied relationships. In this review we discuss applications of imaging sonar among a meta-analysis of 155 studies of aquatic organisms in their environments. We evaluate the performance of imaging sonar for studying inter- and intra-specific interactions, associations with complex and sensitive habitats and low-visibility environments, and to evaluate traditional fisheries sampling gears. We examine the data processing and analytical methods used to refine taxonomic resolution, manage time use and autocorrelation through sub-sampling, extract behavioural metrics applied to ecological processes, and for automating abundance estimates and image classification with the goal of providing a resource for researchers.

Target Acquisition for Collimation System of Wireless Quantum Communication Networks in Low Visibility.

In severe low-visibility environments full of smoke, because of the performance degeneration of the near-infrared (NIR) collimation system of quantum drones communication networks, the improved dual-threshold method based on trend line analysis for long-wave infrared (LWIR) quantum cascade lasers (QCLs) is proposed, to achieve target acquisition. The simulation results show that smoke-scattering noise is a steeply varying medium-high-frequency modulation. At particle sizes less than 4 μm, the traditional dual-threshold method can effectively distinguish the target information from the smoke noise, which is the advantage of the LWIR laser compared to the NIR laser. For detecting lasers with high signal-to-noise ratios (SNRs), the method can achieve good target acquisition, by setting reasonable conventional thresholds, such as 0.7 times the peak intensity and 0.8 times the peak rising velocity. At low SNRs and steep intensity variation, the method can also achieve good target acquisition, by adaptively resetting new thresholds after filtering the detecting laser, such as 0.6 times the peak intensity and 0.6 times the peak rising velocity. The results of this paper will provide a reference for the performance improvement and refinement of the collimation system for wireless quantum communication networks in low visibility.

Zero thermal quenching and suppressed concentration quenching in a highly efficient structure‐confined fluoride near-infrared phosphor

The development of thermally stable and efficient broadband near-infrared (NIR) phosphors suffers from severe hindrance. Herein, a unique anti-fluorescence quenching is realized in a novel Cs2NaGaF6: Cr3+ NIR fluoride phosphor with a strategy of integrating the structural confinement effect with weak electron-phonon coupling effect. Upon blue light excitation, this phosphor exhibits a wide emission band with a peak wavelength of ∼789 nm and a full width at half maximum (FWHM) of 117 nm. Thanks to the structural confinement effect, a large quenching content of 20 mol% and a high internal quantum efficiency (IQE) of 87.4% are achieved, showing a suppressed concentration quenching behavior. Significantly, Cs2NaGaF6: 0.20Cr3+ phosphor possesses an abnormal zero thermal quenching, which could be related with the weaker electron-phonon coupling effect. The emission intensity of Cs2NaGaF6: 0.20Cr3+ at 423 K can maintain up to 101.96% of that at 298 K. A fabricated NIR phosphor-converted light-emitting diode (pc-LED) exhibits an output power of 57.71 mW and a photoelectric conversion efficiency as high as 19.24% at 100 mA. Finally, utilizing a high-power NIR LED as the illuminant for traffic surveillance, significant information can be captured even in low-visibility environments. As a result, Cs2NaGaF6: Cr3+ possesses tremendous prospects in high-power NIR LEDs.

Research on Thermal Infrared Imaging and Positioning Technology for Emergency Rescue at Sea in Bad Weather

In bad weather, the visibility at sea is low, and it is difficult to rescue people and ships in distress. Existing sensors such as visible light cameras and LIDAR will fail in bad weather, so this paper proposes a design scheme for maritime emergency rescue based on thermal infrared cameras. Infrared thermal imaging is a technology that converts infrared thermal radiation into electrical signals and displays them through the screen, which is unaffected in low visibility environments such as bad weather and night and still works normally, and is a great aid to maritime emergency rescue. The accurate identification of maritime vessels is achieved by selecting the YOLOV5 target detection algorithm with high accuracy, the accurate ranging of maritime vessels by monocular ranging algorithm, and finally the whole project is deployed to NVIDIA Jetson AGX Xavier high-performance edge computing device for inference acceleration using TensorRT framework. To verify the effect of different sizes of network structures on the training of the dataset, experiments were conducted on YOLOV5’s s-model, m-model, l-model and x-model, respectively, and the results showed that the detection results of these four models were comparable, however, the inference frame rates did vary greatly. In windows the inference frame rate is 19fps for the s-model, 15fps for the m-model, 9fps for the l-model and 4fps for the x-model. The s-model was chosen to be used in the subsequent study. The s-model was accelerated by TensorRT to increase the frame rate from 19fps to 30fps, enabling real-time detection and ranging functions. The experimental analysis shows that the scheme of this paper is feasible and reliable.

JamTac: A Tactile Jamming Gripper for Searching and Grasping in Low-Visibility Environments.

Humans can feel and grasp efficiently in the dark through tactile feedback, whereas it is still a challenging task for robots. In this research, we create a novel soft gripper named JamTac, which has high-resolution tactile perception, a large detection surface, and integrated sensing-grasping capability that can search and grasp in low-visibility environments. The gripper combines granular jamming and visuotactile perception technologies. Using the principle of refractive index matching, a refraction-free liquid-particle rationing scheme is developed, which makes the gripper itself to be an excellent tactile sensor without breaking its original grasping capability. We simultaneously acquire color and depth information inside the gripper, making it possible to sense the shape, texture, hardness, and contact force with high resolution. Experimental results demonstrate that JamTac can be a promising tool to search and grasp in situations when vision is not available.

Assessing thermal imagery integration into object detection methods on air-based collection platforms

Object detection models commonly focus on utilizing the visible spectrum via Red–Green–Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal Long Wave Infrared (LWIR) (7.5–13.5 µm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and RGB-LWIR fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation, finding that a blended RGB-LWIR model generally exhibits superior performance compared to independent RGB or LWIR approaches. For example, an RGB-LWIR blend only performs 1–5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlay provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms (especially in low visibility environments). This approach has the ability to improve object detection performance for a range of use cases in industrial, consumer, government, and military applications. This research greatly contributes to the study of multispectral object detection by quantifying key factors affecting model performance from drone platforms (including distance, time-of-day and sensor type). Finally, this research additionally contributes a novel open labeled training dataset of 6300 images for RGB, LWIR, and RGB-LWIR fused imagery, collected from air-based platforms, enabling further multispectral machine-driven object detection research.

Real-Time Visual Recognition of Ramp Hand Signals for UAS Ground Operations

We describe the design and validation of a vision-based system that allows the dynamic identification of ramp signals performed by airport ground staff. This ramp signals’ recognizer increases the autonomy of unmanned vehicles and prevents errors caused by visual misinterpretations or lack of attention from the pilot of manned vehicles. This system is based on supervised machine learning techniques, developed with our own training dataset and two models. The first model is based on a pre-trained Convolutional Pose Machine followed by a classifier, for which we have evaluated two possibilities: A Random Forest and a Multi-Layer Perceptron based classifier. The second model is based on a single Convolutional Neural Network that classifies the gestures directly imported from real images. When experimentally tested, the first model proved to be more accurate and scalable than the second one. Its strength relies on a better capacity to extract information from the images and transform the domain of pixels into spatial vectors, which increases the robustness of the classification layer. The second model instead is more adequate for gestures’ identification in low visibility environments, such as during night operations, conditions in which the first model appeared to be more limited, segmenting the shape of the operator. Our results support the use of supervised learning and computer vision techniques for the correct identification and classification of ramp hand signals performed by airport marshallers.

Dynamic Data Augmentation Based on Imitating Real Scene for Lane Line Detection

With the rapid development of urban ground transportation, lane line detection is gradually becoming a major technological direction to help to realize safe vehicle navigation. However, lane line detection results may have incompleteness issues, such as blurry lane lines and disappearance of the lane lines in the distance, since the lane lines may be heavily obscured by vehicles and pedestrians on the road. In addition, low-visibility environments also pose a challenge for lane line detection. To solve the above problems, we propose a dynamic data augmentation framework based on imitating real scenes (DDA-IRS). DDA-IRS contains three data augmentation strategies that simulate different realistic scenes (i.e., shadows, dazzle, and crowded). In this way, we expand from a limited scene dataset to realistically fit multiple complex scenes. Importantly, DDA-IRS is a lightweight framework that can be integrated with a variety of training-based models without modifying the original model. We evaluate the proposed DDA-IRS on the CULane dataset, and the results show that the data-enhanced model outperforms the baseline model by 0.5% in terms of F-measure. In particular, the F-measure of the “Normal”, “Crowded”, “Shadow”, “Arrow”, and “Curve” achieve a 0.4%, 0.1%, 1.6%, 0.4%, and 1.4% improvement, respectively.

Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane

Aiming at the problems of brightness attenuation and contrast reduction in the target image caused by underwater low-visibility environments, a metal target detection method based on split-focus plane polarization imaging is proposed. Firstly, a hybrid enhancement method is proposed to clarify the degraded polarization image. In this study, the GrayWorld method is improved to compensate the attenuation difference of the total light intensity of the polarization image. Variational contrast and saturation enhancement algorithms are used to reduce the underwater scattering effect; secondly, a split-focus plane polarization imaging system is built to complete the control of camera parameters, polarization image acquisition and information processing. Under different underwater low-visibility conditions, polarization imaging of targets with different materials can be realized; finally, an image quality evaluation system is constructed to compare the light intensity and degree of polarization images that are collected by the focal plane polarization imaging system. The polarization characteristics of metal and nonmetal target plates are analyzed. The results show that under the condition of low visibility, the obtained polarization image contrast of the metal target is relatively high, and its EME, information entropy and average gradient are increased by 183.82%, 53.46% and 586.22% on average relative to the image of light intensity. In an underwater low-visibility scene, the method of focal plane polarization imaging proposed in this paper can reduce the difficulty of metal target detection.

Intrusion Detection System using Regulated Patrolling Robots for Apartments

Nowadays robots are incorporated in the job which are often difficult for humans and these robots can be used as an effective alternative for humans. Mobile robots have become a significant topic in the security field Several techniques have been introduced to work with mobile robots and security. Most of these methods are not capable of working in low visibility environments and need to be manually controlled by a person all the time. In this smart security system, a mobile robot acts as an e-patrol in both light and dark environments. The design and implementation of mobile robots consist of three subsystems: The obstacle avoidance, image capturing and alarm indication for theft prevention. By means of which bot can be deployed for surveillance in a defined path with specific intervals.

The quest for ghost gear in the German Baltic Sea: A team effort between WWF, divers, fisherfolk, and public authorities

In this pilot project, World Wild Fund for Nature (WWF) Germany works together with regional divers, fisherfolk and public authorities to reduce the impact of lost fishing gear in the Baltic Sea. If not removed, ghost gear poses a threat to the marine environment and wildlife including seabirds, seals, harbour porpoises and fish. Over decades to centuries, lost fishing nets and ropes shed microplastic fibres into the marine environment. Removing this hazard reduces both the risk of entanglement as well as the contamination of the marine foodweb through ingestion of microplastics and associated chemicals. Identifying lost fishing gear in the marine environment poses one of the largest challenges impeding mitigation through gear retrieval operations. Lost gear can be drifting on the surface, in the water column, or can be sunken to the seafloor as a result of material composition, fouling, and entanglement. In the Baltic Sea, ghost gear is located on the seafloor and not visible during visual surface surveys from vessels. Identifying an efficient search methodology was therefore a key aspect of WWF’s ghost gear project. After trials with different search and retrieval methodologies, WWF Germany found sonar search technology to be the most efficient technique to locate lost gear on the seafloor. Sound waves avoid the limitations faced by divers or visual cameras in low-visibility environments, and a substantially larger area can be covered. In contrast to diving teams focussing on wreck retrievals, the many nets lost on the seafloor remain unnoticed by divers under most circumstances. A combination of sonar search providing exact GPS positions of suspect ghost gear, diver verification through the WWF Ghostdiver App, point-on retrievals with fishing vessels, and manual sorting for waste management provides an efficient methodology for long-term political implementation of regular lost gear retrieval campaigns.

Infrared Sensation-Based Salient Targets Enhancement Methods in Low-Visibility Scenes.

Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.

Low-latency perception in off-road dynamical low visibility environments

Robust systems are required for autonomous driving on non-uniform terrain commonly found in open-pit mines and developing countries. To help narrow the gap in this kind of application, this work proposes a perception system for autonomous vehicles and advanced driver assistance specialized on unpaved roads and off-road environments capable of navigating through rough terrain without a predefined trail. As part of this system, the Configurable Modular Segmentation Network (CMSNet) framework is proposed facilitating the creation of different architectures arrangements. Some CMSNet configurations were ported and trained to segment obstacles and trafficable ground on a new collection of images from unpaved roads and off-road scenarios containing adverse conditions such as night, rain, and dust. It was also performed: an investigation regarding the feasibility of applying deep learning to detect regions where the vehicle can pass through when there is no clear track boundary; a study of how our proposed segmentation algorithms behave in different severity levels of visibility impairment; and an evaluation of field tests carried out with semantic segmentation architectures conditioned for real-time inference. The new dataset (named Kamino) has almost 12,000 new images collected from an operated vehicle with various sensors, including eight cameras capturing synchronized sequences from different points of view. The Kamino dataset has a high number of labeled pixels compared to similar publicly available collections. It includes images collected from an off-road proving ground exclusively assembled for testing the system that emulates an open-pit mine scenario under different adverse conditions of visibility. To achieve embedded real-time inference and allows field tests, many layers of the CMSNet CNN networks were methodically removed and fused using TensorRT, C++, and CUDA. Empirical experiments on two datasets validated the effectiveness of the proposed system. The proposed solution achieves a mIoU of ∼87% (50.40% higher than the PSPNet and 56.21% than the DeepLabV3) on the Kamino dataset, and ∼81 % on the DepScene dataset, reaching up to 29 FPS on the RTX2060 GPU and almost 100 FPS with the optimized configurations, and low standard deviation values with the DrivePX2 computer platform.

Application of LiDAR System on Firefighting and Rescue

Abstract— This paper first introduces about LiDAR/ToF (light detection and ranging based on time of flight) sensor system. Then, the chance of applying LiDAR system on firefighting and rescue is also discussed. In addition, via several experiments of using LiDAR system on various levels of low visibility environment condition (such as strong back-ground light, snow, smoke, foggy, dust cloud), we will conclude the configurations and parameters of LiDAR for suitable choices and decisions when considering applying of LiDAR on firefighting and rescue. Keywords-- Firefighting and Rescue; LiDAR; ToF; Low Visibility Environments; SLAM

Research on rescuers’ training process in toxic / explosive / flammable environments

The paper presents the structuring of physical effort put in by rescuers during training, taking into account the activity specific psychosocial factors, structuring that aimed at streamlining the training process of intervention and rescue personnel in toxic / explosive / flammable environments. Training routes with various degrees of difficulty, allowing the simulation of intervention activities in horizontally and vertically confined spaces, low visibility, high temperature and humidity environments were analysed, for each the specific labour consumption being calculated. A dysfunctional phenomenon caused by the intervention and rescue activity is fatigue, perceived as a body reaction to readjust and restore its functions following intense or repeated body requests for energy consumption. Not only physical fatigue (which can be controlled through exercises) is specific for rescue and intervention activity but also mental fatigue. In addition to physical and technical training, psychosocial training of people who carry out intervention and rescue activities was followed in the training programs.