Ice Detection Research Articles

The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks

Automated Vehicles (AVs) are expected to dramatically reduce traffic accidents that have occurred when using human driving vehicles (HVs). However, despite the rapid development of AVs, accidents involving AVs can occur even in ideal situations. Therefore, in order to enhance their safety, “preventive design” for accidents is continuously required. Accordingly, the “preventive design” that prevents accidents in advance is continuously required to enhance the safety of AVs. Specially, black ice with characteristics that are difficult to identify with the naked eye—the main cause of major accidents in winter vehicles—is expected to cause serious injuries in the era of AVs, and measures are needed to prevent them. Therefore, this study presents a Convolutional Neural Network (CNN)-based black ice detection plan to prevent traffic accidents of AVs caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, we augmented image data and learned road environment images. Tests showed that the proposed CNN model detected black ice with 96% accuracy and reproducibility. It is expected that the CNN model for black ice detection proposed in this study will contribute to improving the safety of AVs and prevent black ice accidents in advance.

A heat-pulse method for detecting ice formation on surfaces

Early detection of ice formation on surfaces is a crucial requirement in many applications such as wind turbines, aeroplane’s wings, and radar domes. This paper presents the concept of a heat-pulse ice detection system. Its principle consists of recording the temperature evolution and assessing the relaxation time after the surfaces are subject to a heat excitation generated by an electrical pulse. The sensor comprises thin graphene films embedded in pre-preg layers of aeronautical grade as well as thermocouples to record the temperatures profiles at given locations. The criterion of ice detection exploits the high thermal capacity of ice compared to the air to capture the formation of icy spots. Numerical simulations and comparison with experimental investigations have been carried out to validate the principle and criteria of the ice detection system.

Modified Microwave Sensor with a Patterned Ground Heater for Detection and Prevention of Ice Accumulation.

Ice accumulation on aircraft is known to negatively impact the aerodynamic and mechanical operation, sometimes resulting in catastrophic failure. Recently, microwave resonators have gained interest as durable and reliable frost and ice detectors. Here, a microwave resonator sensor with built-in heating capability patterned into the ground plane was designed, fabricated, and tested to investigate real-time ice and frost growth. Sensing was performed on surfaces with anti-icing coatings to quantitatively analyze the effectiveness of these materials. The sensor was also tested to determine its ability to evaluate different deicing methods. The sensor itself was a split-ring resonator (SRR) operating at 5.82 GHz, which could effectively distinguish between water and ice by detecting changes in the dielectric properties on or around its surface. This application was particularly suited for an SRR due to the extreme difference between the relative permittivity of water (ε = 90) and ice (ε = 3.2) at 5 GHz and 0 °C. The results from this sensor can be used to determine the holdover time of various coatings to resist ice formation. This study validates the use of SRRs as ice detection sensors for applications where ice and frost are of great interest, such as on aircraft, roads, or walkways.

Simultaneous ice detection and removal based on dielectric barrier discharge actuators

This paper presents a new technique for ice detection using dielectric barrier discharge (DBD) plasma actuator. The DBD ice sensor consists of a pair of electrodes and a test capacitor. Both numerical simulation and experimental studies were conducted. The electrical characteristics of the DBD actuator were measured in the presence of ice. The presence of ice (or water) leads to an increase of the capacitance compared to air and can thus be detected. Dynamic variation of the actuator capacitance and evolution of ice layer melting process was measured. Moreover, multi-DBD actuator was studied for generating icing map on the surface.

Acoustic Characterization of Glaze and Rime Ice Structures on an Oscillating Airfoil via Fully Unsteady Simulations

The development of low-cost and simple technologies to improve pilot awareness of icing environments is crucial to improve the safety of rotorcraft, and especially those with limited icing clearance which are admittedly operating within icing environments without full icing clearance. An acoustic characterization of glaze and rime ice structures is hereby introduced to begin to quantify different ice shape noise signatures which directly transcend from the iced performance characteristics to develop acoustic ice detection technologies. The feasibility of the detection technique is assessed for fully unsteady simulations of ice accretion on an oscillating, two-dimensional airfoil. This work focuses on the computational modeling of the experimental database of a rotor airfoil with pitching motion during icing conditions from the NASA Glenn Icing Research Wind Tunnel and computing the resultant noise signals and analyzing their topology.

Denoising Sentinel-1 Extra-Wide Mode Cross-Polarization Images Over Sea Ice

Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal–vertical, HV or vertical–horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this article, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e., multiplicative noise) at the subswath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in the marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization synthetic aperture radar (SAR) images in wide swaths for intensive sea ice monitoring in polar regions.

Discriminative feature learning for blade icing fault detection of wind turbine

The early detection of blade icing is gaining increasing attention due to its importance in guaranteeing wind turbine safety and operation efficiency. In this study, a wind turbine icing fault detection method based on discriminative feature learning is proposed. First, a stacked autoencoder (SAE) is trained to generate representations, which utilizes a large amount of normal operating data, as well as time series correlation information. Second, discriminative features are obtained by combining the original data, SAE-extracted features, and the residual vector. Third, the sparse linear discriminant analysis is performed on the discriminative features to achieve simultaneous feature selection and dimension reduction. Finally, the wind turbine operation status is examined using the learned discriminative feature. The proposed discriminative feature learning-based fault detection scheme is tested on a benchmark wind turbine icing dataset. Results of the comparative trial verify the feasibility and superiority of the proposed method.

Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images

Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO2 is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces.

Method for black ice detection on roads using tri-wavelength backscattering measurements.

This paper provides a method of detecting black ice on a road surface by multiwavelength noncontact optical technology. The laser sources, wavelengths of 1310, 1430, and 1550 nm, were irradiated on the road surface. Then, we define the ratio of the backscattering power under a certain condition to the backscattering power under dry conditions as the normalized reflectance. It is found that the normalized reflectance under dry, water, black ice, icy, and snowy conditions is different. Therefore, the normalized reflectance can be used to identify black ice on a road surface.

Pitot Tube-Based Icing Detection: Effect of Ice Blocking on Pressure

This study aims at addressing a problem on icing detection for Unmanned Aerial Vehicle (UAV for short) because traditional icing detection methods are costly and bulky. Toward this end, a pitot-based icing detection method is proposed, and the effect of different types of icing blocking on pressure is firstly reported. An icing detection system based on the pitot tube is designed and fabricated. Icing wind tunnel results indicate that if the pitot tube is blocked by glaze ice, then the total pressure of the pitot tube decreases gradually and remains unchanged and less than static pressure. However, if the pitot tube is blocked by rime ice, then the total pressure drops to the same level as the static pressure. If the pitot tube is blocked by non-ice organic materials, then the total pressure suddenly drops to the same level as the static pressure and remains unchanged. Furthermore, if the pitot tube contacts the water droplets but does not freeze, the total pressure output value fluctuates slightly. The effect of icing on pressure is caused by differences in ice microstructure, temperature, and flow velocity. At the same time, the proposed method offers a facile and low-cost approach for UAV icing detection.

Active/Passive Multiple Polarization Sea Ice Detection During Initial Freeze-Up

An algorithm for ice/water detection of ice less than 30 cm in thickness (thin ice) using dual-polarized synthetic aperture radar (SAR) and passive microwave data has been developed based on insights from previous work. The frequency chosen from those available was in the C-band. For the radiometer layer ice concentrations, land contaminated pixels were removed. For the SAR layers, a novel approach to their use in ice detection was developed using the coefficient of variation (COV), defined as the ratio of the standard deviation of the normalized radar cross section, σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , to the mean σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . The COV reduced the complicated signatures of breaking waves and thin ice to bright ice pixels on a dark water background. The co-pol and cross-pol COV layers and the radiometer layer were merged according to physics-based rules, except in regions adjacent to land or the ice edge where only the SAR layers were valid. The detection algorithm was applied to 233 Sentinel 1a and 1b dual-polarization images of the freeze-up from 2015, 2016, 2018, and 2019 in the Beaufort and Chukchi Seas. The synergistic ice retrievals showed improved agreement with manual operational analyses in the marginal ice zone over other automated operational products.

ICE DETECTION ON AIRPLANE WINGS USING A PHOTOGRAMMETRIC POINT CLOUD: A SIMULATION

Abstract. This study describes some tests carried out, within the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of ice), for the geometrical ice detection on airplane wings. The purpose of these analysis is to estimate thickness and shape of the ice that an RGB sensor is able to detect on large aircrafts as Boeing 737-800. However, field testing are not available yet, therefore, in order to simulate the final configuration, a steel panel has been used to reproduce the aircraft surface. The adopted methodology consists in defining a reference surface and modelling its 3D shape with and without ice through photogrammetric acquisitions collected by a DJI Mavic Air drone hosting a RGB camera and processed by Agisoft Metashape software. The comparison among models with and without the ice has been presented and results show that it is possible to identify the ice, even though some noise still remains due to the geometric reconstruction itself. Finally, using 3dReshaper and Matlab software, the authors develop various analysis defining the operative limits, the processing time, the correct setting up of Metashape for a more accurate ice detection, the optimization of the methodology in terms of processing time, precision and completeness. The procedure can certainly be more reliable considering the usage of the hyperspectral sensor technique as future implementation.

LAKE ICE DETECTION FROM SENTINEL-1 SAR WITH DEEP LEARNING

Abstract. Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN).We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores &gt;90% on average, and &gt;84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

Machine-learning classification of debris-covered glaciers using a combination of Sentinel-1/-2 (SAR/optical), Landsat 8 (thermal) and digital elevation data

Debris cover on glacier surfaces hampers the accurate detection of debris-covered ice using traditional techniques based on image band ratios. Therefore, this study tests a new automatic classification scheme for hierarchical mapping of glacier surfaces based on machine learning classifiers including k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting (GB), decision tree (DT), random forest (RF) and multi-layer perceptron (MLP). Several raster layer combinations (synthetic aperture radar (SAR) coherence image derived from Sentinel-1 data, visible near-infrared to short wave infrared bands from Sentinel-2, thermal information from Landsat 8 and geomorphometric parameters from the Advanced Land Observing Satellite (ALOS) World 3D 30 m mesh (AW3D30) digital elevation model) were tested to delineate the debris-covered glaciers in the Gilgit-Baltistan, Pakistan and Shaksgam valley, China. The highest over classification accuracy (97%) was obtained using the RF classifier (followed by the GB and SVM with radial basis function kernel) and utilizing all of the multisensor Sentinel/Landsat/ALOS data. Notably, the RF classifier showed to be robust to parameter settings, fast and accurate for mapping debris-covered ice. GB classifier showed similar performance as RF despite it has a moderately lower accuracy compared to RF. Although SVM classifier has a slower in the speed of tuning hyper-parameter, it still performs the third-best classification accuracy. As the multisensory data we used is freely and (near-)globally available, our methodology potentially could be applied for precise delineation of debris-covered glaciers in other areas.

De-Icing Device With Self-Adjusting Power Consumption and Ice Sensing Capabilities

Ice accumulation on mechanical structures pose series of safety challenges in daily life. Therefore, it is important to detect the presence of ice and subsequently remove it, thereby hindering its accumulation. We demonstrate an electrode design which can function concurrently as an ice detection and de-icing system. A dielectric coated interdigitated device was fabricated. Ice was generated using a Peltier cooler. By measuring the impedance change of the device with frequency under different conditions (dry and wet), ice was detected. Extent of ice formation can be estimated from the impedance values. Using the same interdigitated electrode device, de-icing was also established by applying a high frequency (50 kHz) and high voltage (200 V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> ) AC signal. The output power of the system was calculated using an equivalent circuit model which also showed that the de-icing system delivers negligible output power in the absence of ice making it energy efficient. For performing de-icing on curved structures, the technique was demonstrated using flexible substrates where interdigitated electrodes were printed on flex polyamide substrates. [2019-0254].

Influence of Ice Accumulation on the Structural Dynamic Behaviour of Composite Rotors

The implementation of wind turbines as a source of sustainable, renewable energy is increasing. Although the prospects of renewable energy development are promising, ice accumulation on turbine blades still stands as a major operational issue. Excessive ice mass on turbine blades can lead to damage or total failure of the blades but also to the nacelle gearbox and to the generator. Therefore, a detailed understanding of the ice accumulation on the composite blades and the effect on their modal properties can be beneficial and give an insight before catastrophic failure occurs. On the one hand, it can be understood how ice accumulation affects the profile of the composite surface to consequently identify the relationships between ice accumulation and mass, stiffness, as well as damping distribution. On the other hand, by mapping these relationships, the first step is performed towards solving the inverse problem, which is to identify critical ice accumulation at an early stage based on modal properties. In this way, ice detection and identification can provide significant savings in time and costs. To investigate the basic relationships between ice accumulation and structural dynamic behaviour, an experimental rotor test rig is developed, combining an electromotor with a climate chamber. The test rig simulates various environmental conditions under different rotational speeds and ice distributions. The first experimental tests are performed on a glass-fibre reinforced epoxy rotor, and several measurements are conducted deploying different kinds of icing and temperature conditions. Various sensors are applied to characterise the vibration response as well as mass, type, and spatial distribution of the ice. The results are evaluated with regard to identifying unknown relations between ice accumulation and the structural dynamic behaviour of composite rotors.

Rotation invariant angle-density based features for an ice image classification system

One of the natural disasters which cause economic loss and are a serious threat to society are ice covering phenomena for overhead transmission power lines. This paper presents a new method for ice and non-ice image classification to improve ice detection results. The proposed method uses wavelet decomposition to extract robust features, such as those invariant to rotation, scaling and thickness of ice for classification. The proposed approach estimates the average of the high frequency sub-bands for each level. Then it obtains Canny edge components for the average wavelet image at each level. Our approach studies shape of edge components to identify the presence of ice. To achieve this, the proposed method finds the major and minor axes for each edge component, and then draws parallel lines to the major and minor axes over the edge components. For each parallel line to the major and minor axes, the proposed approach further extracts angle and density-based features for pixels that fall on the parallel lines to the major and minor axes. Next, our method selects features from each average wavelet image and further calculates the mean for the feature vectors corresponding to the level, which results in a feature matrix. Finally, the feature matrix is fed to a Multi-Layer Perceptron Neural Network for classifying ice and non-ice images. Experimental results on a diversified dataset and comparative study with an existing method show that the proposed method is useful for accurate ice detection with better accuracy.

Characteristics and Distribution of efficient ice nucleating particles in rainwater and soil

Ice nucleating particles (INPs) might influence precipitation and are related to the hydrologic cycle by initiating ice formation. The contribution of efficient INPs (and almost all biological particles) is significant in catalyzing the freezing of ice at relatively warm temperatures (> −10 °C). However, to what extent biological matter functions as INPs in clouds is poorly understood and the large gap in investigating the properties of ice nucleating ability in rainwater and variation in INPs during a single precipitation event needs to be addressed. In this study, we tested the ice nucleating activity in rainwater and soil to probe the characteristics and distribution of INPs and tried to trace the possible sources of INPs. We suggested that the biological INPs dubbed efficient INPs are commonly present in rainwater and soil at > −10 °C, and it is surprising that unknown efficient INPs, probably derived from microbes, were shown to dominate at warmer temperatures by detection of the potential known ice nucleating active genera through high-throughput sequencing analysis. Furthermore, certain heat-resistant efficient INPs were found in rainwater and a few nanoscale efficient biological INPs were retained partially in soil. Moreover, we also found that nonlocally sourced inefficient INPs dominated in precipitation induction and efficient INPs became gradually more involved as precipitation proceeded by comparing the diverse periods of one entire heavy rainfall event.

Detection of Ice 0 in Various Synthetic and Natural Environments

Detection of Ice 0 in Various Synthetic and Natural Environments

Evaluation of Functionalized Coatings for the Prevention of Ice Accretion by Using Icing Wind Tunnel Tests

Ice accretion presents serious safety issues, as airplanes are exposed to supercooled water droplets both on the ground and while flying through clouds in the troposphere. Prevention of icing is a main concern for both developers and users of aircraft. The successful solution of this problem implies the combination of active and passive methods and the use of advanced sensors for early detection of icing and monitoring of ice accretion and de-icing processes. This paper focuses on the development of passive solutions. These include advanced anti-icing coatings deposited by a variety of chemical methods including sol-gel, advanced paints based on polyester combined with fluorinated derivatives and applied by electrostatic spray deposition and conventional silicone-based paints modified by adding alumina nanoparticles. Water contact angle has been measured in all cases, demonstrating the hydrophobic character of the coatings. An ice accretion test has been carried out in a laboratory scale icing wind tunnel (IWT) located in a cold climate chamber. Three different studies have been undertaken: ice accretion measurement, durability of the anti-icing behavior after several icing/de-icing cycles and ice adhesion testing by means of the double lap shear test (DLST) methodology. All the studied coatings have shown significant anti-icing behavior which has been maintained, in some cases, beyond 25 cycles. Although these results are still far from any possible application for aeronautic components, they provide interesting insights for new developments and validate the laboratory scale tests.