• Title/Summary/Keyword: Black ice detection

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Multi-Scale Dilation Convolution Feature Fusion (MsDC-FF) Technique for CNN-Based Black Ice Detection

  • Sun-Kyoung KANG
    • Korean Journal of Artificial Intelligence
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    • v.11 no.3
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    • pp.17-22
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    • 2023
  • In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

Black Ice Detection Platform and Its Evaluation using Jetson Nano Devices based on Convolutional Neural Network (CNN)

  • Sun-Kyoung KANG;Yeonwoo LEE
    • Korean Journal of Artificial Intelligence
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    • v.11 no.4
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    • pp.1-8
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    • 2023
  • In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

Road Environment Black Ice Detection Limits Using a Single LIDAR Sensor (단일 라이다 센서를 이용한 도로환경 블랙아이스 검출 한계)

  • Sung-Tae Kim;Won-Hyuck Choi;Je-Hong Park;Seok-Min Hong;Yeong-Geun Lim
    • Journal of Advanced Navigation Technology
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    • v.27 no.6
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    • pp.865-870
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    • 2023
  • Recently, accidents caused by black ice, a road freezing phenomenon caused by natural power, are increasing. Black ice is difficult to identify directly with the human eye and is more likely to misunderstand it as standing water, so there is a high accident rate caused by car sliding. To solve this problem, this paper presents a method of detecting black ice centered on LiDAR sensors. With a small, inexpensive, and high-accuracy light detection and ranging (LiDAR) sensor, the temperature and inclination angle are set differently to detect black ice and asphalt by setting different reflection angles of asphalt and black ice differently in temperatures and inclinations. The LIDARO carried out in the study points out that additional research and improvement are needed to increase accuracy, and through this, more reliable black ice detection methods can be suggested. This method suggests a method of detecting black ice through early system design research by preventing accidents caused by black ice in advance.

Preliminary Study on Black-Ice Detection Using GPS Ground Reflection Signals

  • Young-Joo Kwon;Hyun-Ju Ban;Sumin Ryu;Suna Jo;Han-Sol Ryu;Yerin Kim;Yun-Jeong Choi;Sungwook Hong
    • Journal of the Korean earth science society
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    • v.45 no.4
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    • pp.318-326
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    • 2024
  • Black ice, a thin and nearly invisible ice layer on roads and pavements, poses a significant danger to drivers and pedestrians during winter due to its transparency. We propose an efficient black ice detection system and technique utilizing Global Positioning System (GPS)-reflected signals. This system consists of a GPS antenna and receiver configured to measure the power of GPS L1 band signal strength. The GPS receiver system was designed to measure the signal power of the Right-Handed Circular Polarization (RHCP) and Left-Handed Circular Polarization (LHCP) from direct and reflected signals using two GPS antennas. Field experiments for GPS LHCP and RHCP reflection measurements were conducted at two distinct sites. We present a Normalized Polarized Reflection Index (NPRI) as a methodological approach for determining the presence of black ice on road surfaces. The field experiments at both sites successfully detected black ice on asphalt roads, indicated by NPRI values greater than -0.1 for elevation angles between 45° and 55°. Our findings demonstrate the potential of the proposed GPS-based system as a cost-effective and scalable solution for large-scale black ice detection, significantly enhancing road safety in cold climates. The scientific significance of this study lies in its novel application of GPS reflection signals for environmental monitoring, offering a new approach that can be integrated into existing GPS infrastructure to detect widespread black ice in real-time.

Study of Black Ice Detection Method through Color Image Analysis (컬러 이미지 분석을 통한 블랙 아이스 검출 방법 연구)

  • Park, Pill-Won;Han, Seong-Soo
    • Journal of Platform Technology
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    • v.9 no.4
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    • pp.90-96
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    • 2021
  • Most of the vehicles currently under development and in operation are equipped with various IoT sensors, but some of the factors that cause car accidents are relatively difficult to detect. One of the major risk factors among these factors is black ice. Black ice is one of the factors most likely to cause major accidents, as it can affect all vehicles passing through areas covered with black ice. Therefore, black ice detection technique is essential to prevent major accidents. For this purpose, some studies have been carried out in the past, but unrealistic factors have been reflected in some parts, so research to supplement this is needed. In this paper, we tried to detect black ice by analyzing color images using the CNN technique, and we succeeded in detecting black ice to a certain level. However, there were differences from previous studies, and the reason was analyzed.

Black Ice Formation Prediction Model Based on Public Data in Land, Infrastructure and Transport Domain (국토 교통 공공데이터 기반 블랙아이스 발생 구간 예측 모델)

  • Na, Jeong Ho;Yoon, Sung-Ho;Oh, Hyo-Jung
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.7
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    • pp.257-262
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    • 2021
  • Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

A Black Ice Recognition in Infrared Road Images Using Improved Lightweight Model Based on MobileNetV2 (MobileNetV2 기반의 개선된 Lightweight 모델을 이용한 열화도로 영상에서의 블랙 아이스 인식)

  • Li, Yu-Jie;Kang, Sun-Kyoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.12
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    • pp.1835-1845
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    • 2021
  • To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

A Black Ice Detection Method Using Infrared Camera and YOLO (적외선 카메라와 YOLO를 사용한 블랙아이스 탐지 방법)

  • Kim, Hyung Gyun;Jang, Min Seok;Lee, Yon Sik
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.12
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    • pp.1874-1881
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    • 2021
  • Black ice, which occurs mainly on the road, vehicle traffic bridges and tunnel entrances due to the sub-zero temperature due to the slip of the road due to heavy snow, is not recognized because the image of asphalt is transmitted in the driver's view, so the vehicle loses braking power because it causes serious loss of life and property. In this paper, we propose a method to identify the black ice by using infrared camera and to identify the road condition by using deep learning to compensate for the disadvantages of existing black ice detection methods (artificial satellite imaging, checking the pattern of slip by ultrasonic reception, measuring the temperature of the road surface, and checking the difference in friction force of the tire during vehicle driving) and to reduce the size of the sensor to detect black ice.

Proposal of a Black Ice Detection Method Using Infrared Camera and YOLO for Reducing of Traffic Accidents (교통사고 경감을 위한 적외선 카메라와 YOLO를 사용한 블랙아이스 탐지 방법 제안)

  • Kim, Hyunggyun;Jang, Minseok;Lee, Yonsik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.416-421
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    • 2021
  • In case of the road slips due to heavy snow and the temperature drops below 0 degrees, black ice which mainly occurs on the road, bridges for vehicles, and tunnel entrances, is not recognized by the driver's view because the image of the asphalt is transmitted through it. So cars' slip situation occurs, which leads to a big traffic accident and a large amount of loss of life and property. This study proposes a method to check the road condition using an infrared camera and to identify black ice through deep learning.

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Proposal of a Black Ice Detection Method Using Vehicle Sensors to Reduce Traffic Accidents (교통사고 경감을 위한 차량 센서를 사용한 블랙아이스 탐지 방법 제안)

  • Kim, Hyung-gyun;Kim, Du-hyun;Baek, Seung-hyun;Jang, Min-seok;Lee, Yonsik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.524-526
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    • 2021
  • As the invention of automobiles and construction of roads for vehicles began, the occurrence of traffic accidents began to increase. Accordingly, efforts were made to prevent traffic accidents by changing the road construction method and using signal systems such as traffic lights, but until now, numerous human and property damages have occurred every year due to traffic accidents caused by freezing of the road due to bad weather. In this paper, we propose a method of transmitting ice detection data detected using vehicle sensor data to vehicle navigation to reduce traffic accidents caused by road freezing.

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