In this chapter, we consider existing detection methods for black ice and studies using CNN in the transportation field to derive the differentiation of this study.
2.1. Black Ice Detection Methods
Methods for detecting black ice include sensors [
10,
11,
12], sound waves [
13,
14], and light sources [
15]. Habib Tabatabai et al. (2017) [
10] conducted a study to detect black ice, ice and water in roads and bridges using sensors embedded in concrete. In this study, a sensor that detects the road surface condition was proposed through the change of electrical resistance between stainless steel columns inside concrete. As a result of conducting experiments under various surface conditions, it was suggested that the proposed sensor can effectively detect the road condition, thereby preventing various accidents. Nuerasimuguli ALIMASI et al. (2012) [
11] conducted a study to develop a black ice detector consisting of an optical sensor and an infrared thermometer. The study was conducted in Route 39 around Sekihoku Pass, Hokkaido, and was conducted on a total of six road conditions (dry, wet, “sherbet”, compact snow, glossy compacted snow, black ice) and diffuse reflection and reflection. As a result of the experiment, black ice had a large specular reflection (
) and a small diffuse reflection (
) was measured, resulting in a low (
/
) value. Youngis E. Abdalla et al. (2017) [
12] proposed a system for detecting black ice using Kinect. The types of ice (Soft Ice, Wet Snow, Hard Ice, Black Ice) were classified and the thickness and volume of the ice were measured using Kinect. Experiments have shown that the types of ice formed in the range of 0.82 m to 1.52 m from the camera can be distinguished, and the error rate of measured thickness and volume is very low, suggesting that black ice can be detected by utilizing Kinect. Xinxu Ma et al. (2020) [
15] studied a black ice detection method using a three-wavelength non-contact optical technology. The study conducted an experiment to distinguish dry, wet, black ice, ice and snowy conditions using three wavelengths (1310 nm, 1430 nm, 1550 nm). As a result of the experiment, it was confirmed that black ice was detected through the reflectance of each wavelength, and it was suggested that it can be used as basic data for the development of equipment to detect road conditions.
2.2. Deep Learning Applications to Intelligent Transportation
Artificial Intelligence (AI) methodologies are currently being used in various fields, and CNN studies using image data for the detection of vehicles and pedestrians, detection of traffic signs, and detection of road surface are actively carried out in the transportation field.
First of all, for vehicle and pedestrian detection, studies using AlexNet [
16,
17], VGG (Visual Geometry Group) 16 [
18], Mask R-CNN [
19], and Faster R-CNN [
20,
21] existed, and a comparative analysis of the performance of Faster R-CNN and YOLO (You Only Look Once) [
22,
23]. Lele Xie et al. (2018) [
24] conducted a vehicle license plate detection study at various angles using CNN-based Multi-Directive YOLO (MD-YOLO). The study proposed an ALMD-YOLO structure combining CNN and MD-YOLO, and compared the performance of various models (ALMD-YOLO, Faster R-CNN, SSD (Single Shot multibox Detector), MD-YOLO, etc.) and found that the newly proposed ALMD-YOLO had the best performance. It also suggested that the simple structure of the model reduced the computational time and that a high-performance multi-way license plate detection model could be established. Ye Yu et al. (2018) [
25] proposed a CNN-based Feature Fusion based Car Model Classification Net (FF-CMNET) for the precise classification of vehicle models. The above study utilized FF-CMNET, which combines UpNet to extract the upper features of the car’s frontal image and DownNet to extract the lower features. Experiments have shown performance better than traditional CNN methodologies (AlexNet, GoogLeNet, and Network in Network (NIN)) in terms of extracting the car’s fine features. M. H. Putra et al. (2018) [
26] conducted a study using YOLO to detect people and cars. Unlike the traditional YOLO structure, the above study proposed a modified YOLO structure using seven convolutional layers and compared their performance. As a result of the study, the modified YOLO’s 11 × 11 grid cells model had a lower mAP compared to the traditional YOLO model, but had better processing speed. In addition, tests with actual images showed that small-sized people and cars could be extracted.
Second, in the case of traffic sign detection, there have been many studies using the basic CNN structure [
27,
28,
29,
30], Mask R-CNN [
31,
32], and Faster R-CNN [
33,
34]. Rongqiang Qian et al. (2016) [
35] conducted a study using Fast R-CNN to recognize traffic signs on road surfaces. To enhance the performance of the model, the experiment was conducted by utilizing MDERs (Maximally Stable Extremal Regions) and EdgeBoxes algorithms in the object recognition process. The results of the experiment showed that the Recall rate was improved, with an average precision of 85.58%. Alexander Shustanov and Yakimov, P. (2017) [
36] conducted a CNN model design study for real-time traffic sign recognition. The study used modified Generalized Hough Transform (GHT) and CNN, with 99.94% accuracy. It was also confirmed that the proposed algorithm could process high-definition images in real time and accurately recognize traffic signs farther away than similar traffic sign recognition systems. Lee, H.S. and Kim, K. (2018) [
37] conducted a study using CNN to recognize the boundaries of traffic signs. They designed CNN based on SSD architecture, and unlike previous studies, they proposed a method of estimating the positions of signs and converting them into boundary estimates. Experiments have confirmed that various types of traffic sign boundaries can be detected quickly.
Finally, studies of road surface detection were reviewed to identify road surface conditions and to detect road cracks. Juan Carrillo et al. (2020) [
38] and Guangyuan Pan et al. (2020) [
39] are both studies that identify road surface conditions. The above studies divided the data into three and four classes and compared the performance of the CNN model. Studies found that up to 91% accuracy was derived, and CNN showed excellent performance in road surface identification. In the road crack detection study, Janpreet Singh et al. (2018) [
40] conducted a study using Mask R-CNN to detect road damage in images taken with smartphones. The data utilized 9053 road damage images taken with smartphones, and the CNN’s structure utilized Mask R-CNN. Experiments have confirmed that road damage is detected effectively, showing high accuracy and 0.1 s processing speed. Zheng Tong et al. (2018) [
41] conducted a study to classify the length of asphalt cracks using DCNN (Deep Convolutional neural networks). Data collection was conducted in various places and weather conditions, and the data were divided into eight classes from 0 cm to 8 cm in 1 cm increments. As a result of the experiment, the accuracy was 94.36% and the maximum length error was 1 cm, and it was suggested that the length of the crack can be classified as well as the existence of a simple crack. Baoxian Li et al. (2020) [
42] conducted a study using CNN to classify road crack types. Road cracks were classified into a total of five types (non-crack, transverse crack, longitude crack, block crack, alligator crack) and four models were designed using the basic CNN structure. As a result of the experiment, the accuracy of the four models designed was 94% or more. It was also confirmed that CNN, which has a 7 × 7 size of the reactive field, was the best choice for crack detection.
2.3. Summary
In summary, it was confirmed that various studies using black ice detection research and CNN in the transportation field are in progress. In addition, a study on CNN in the transportation sector has been conducted to detect the most important objects that make up road environment, such as pedestrians, vehicles, traffic signs, and road surfaces, and object detection using CNN shows a fast processing speed and high accuracy. In spite of such studies, it is expected that there is a limit to preventing black ice accidents in advance due to problems such as the installation of traditional black ice detection systems. Accordingly, this study proposes a method to detect black ice by identifying road conditions based on the CNN technique to prevent black ice accidents in AVs