## 2 Current Research on Data Acquisition, Refinement, and Quality Evaluation  
### AI Research Imitating Human Visual Information  
In general, an ordinary image or a cut-frame image from a video clip is a good material for AI to learn. As shown in Figure 2, until 2008, during which the early stage of data acquisition development occurred, AI R&D was conducted in terms of simple image classification. During this period, data acquisition did not require many human resources to create training data since labeling consisted of simply adding text labels to images. Later, object detection was developed, which made it possible to enhance the accuracy of AI. In object detection, it is an essential task to annotate images, indicating the parts needed to learn from the images [7]. During the early stage of annotation, the data used round boxes for image classification, but efficiency was enhanced following the development of the ability to identify several objects on one image, as shown in Figure 3. Nonetheless, if the image classification data are used without considering annotation, as shown in Figure 3, the advantages of annotation cannot be exploited, which leads to no significant changes in the accuracy of AI. Consequently, researchers developing object detection had to acquire new image data corresponding to the method shown in Figure 3b, and they had difficulties in reconducting atypical refinement accordingly with the AI model to be applied. Figure 3: Difference between (**a**) an example of learning data collected in the era when image classification techniques were dominant and (**b**) learning data collected after object detection was developed. (**a**) Data can also be used for object detection, but they are less efficient and may lead to inaccurate results. Figure 2: Milestones of object detection techniques and the evolution of hardware (GPU). In 2012, researchers who attempted to enhance the accuracy of an AI model developed instance segmentation, a type of object detection that categorizes classes per pixel. Most researchers nowadays apply this method, and it shows excellent performance [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 30; 31; 32; 33; 34]. Since instance segmentation specifies parts on the image needed for learning by pixel, labeling is mostly performed in polygonal form, as shown in Figure 4d. This case requires more workload than required by the existing round boxing. To make up for this, the so-called auto-labeling methods started to be developed, ultimately reaching the level where labeling is carried out on parts without the need to click on certain features, such as trees and cars [35]. Nevertheless, many errors occurred, so revision by personnel was needed, and this required additional acquisition and learning on images in order to perfectly recognize the target. An additional amount of acquisition differs in terms of target accuracy, and figures may vary depending on the engineer. Acquisition methods are ramified as online collection through web surfing and offline collection by shooting [29]. Online collection is carried out by conducting searches on the image server of the web established by people or by conducting automatic web searches through surfing. Due to problems regarding copyrights, the industry prefers offline acquisition, and R&D institutes combine on- and off-line methods to reduce the development schedule and costs. The acquired data are called source data and are in an incompatible state for AI training at the current status quo. After conducting data cleansing on the source data needed for training, the data must be revised to correspond with the model to be developed, and the task incurred here is called data refinement. In brief, refinement includes various steps, from creating a category folder and classifying images to adjusting the image size, changing the resolution, blurring for privacy, and binarizing to simplify color, and the images go through labeling processes such as image boxing to indicate the target object on the image, and segmentation.