# Pothole Classification Model Using Edge Detection in Road Image
## 2 Related Work  
### Image Object Detection Algorithm  
As artificial intelligence and vision technology continue to develop, they are leading innovation in various fields. Among them, the field of artificial intelligence based on visual data using videos and images is used for cancer diagnosis, joint diagnosis, and terrain analysis through object recognition and detection to help improve lives. Based on CNN (Convolution Neural Network), the visual data analysis can classify images, detect objects and generate images. CNN aims to reduce the complexity of the model and extract significant features by applying a convolution operation [17]. In visual data, object detection is a technique to find a candidate region for a detection target to recognize a specific target and to predict the type and location of the object (bounding box) [18; 19]. The algorithms for this include R-CNN [20], Fast-R-CNN [21], Faster-R-CNN [22], and YOLO [23]. R-CNN (Regions with Convolutional Neural Networks) is divided into three stages to detect objects. The first stage is the Region proposal stage. This is the process of extracting a region from an image without classifyingthe target class. The second step is a stage of extracting the feature vector. This removes the region proposal area from the original image, generates it as the same size, and extracts features using CNN. In the last step, the features are classified. Since R-CNN performs CNN calculation as many as the number of region proposal areas per image data, it has the disadvantage of very slow speed and high complexity [20]. Fast-R-CNN is a method that improved the speed problem of R-CNN. This uses the Rol Pooling (Region of Interest Pooling) method. Rol Pooling applies convolution only once to the input image instead of performing convolution operation for every region proposal area and extracts features to identify objects with Rol Pooling. Therefore, Fast-R-CNN is a method of finding the feature by applying the computed feature and Rol Pooling only once to the input image [21]. Faster-R-CNN can design a network that can generate region proposals inside convolution to improve the time required and directly generate accurate region proposals. Faster-R-CNN is an algorithm that adds a region proposal network to Fast-R-CNN [22]. YOLO is an algorithm that predicts the type and location of an object by viewing the image data once using the bounding box and classification probability of the image data as a single regression problem. This is an algorithm suitable for analyzing real-time visual data because it is fast as a simple process. It is also easy to understand what objects exist in the image and where they are located by learning the data only once. Since YOLO learns the general characteristics of objects, it can be predicted even when new data is entered [23].  
Accordingly, [PERSON] et al. [24] proposed a real-time object detection algorithm in the video. The proposed algorithm trained the Fast-YOLO model to preprocess the image to remove the background and obtain object information. In addition, the number of parameters is reduced by improving the existing YOLO using the convolution operation based on the GoogLeNet structure. Accordingly, the time required for object detection was reduced. In addition, [PERSON] et al. [25] proposed a multi-label classification method to improve object detection. In order to improve object detection using R-FCN, the proposed method uses multiple classifications to carry out end-to-end training and testing. Thus, the problem of classification is solved through the approximate location information about the object using the feature result generated using the attention mechanism. Therefore, the box level feature and the label feature are combined to improve the accuracy of object detection. Figure 1 shows the image object detection process.