## 3 Comparison of mAP According to Data Pre-Processing Proficiency
### AI Model Selection  
The construction waste that was studied in this paper was dirty in the original images. This was because many of the materials, such as concrete, timber, and plastic, had lost their original shape and were mixed in a broken state; in many cases, they could not even have been differentiated by a human. Therefore, the objectives of this research were to assist in accurately and efficiently differentiating construction waste using AI. Generally, there are four methods for image recognition and object verification in AI: classification, localization, object detection, and instance segmentation. The factors considered in image recognition methods to develop a construction waste recognition model are as follows. Classification is a method to simply classify objects in an image (Figure 1a). An additional coding process is required in order to check the location of the object found in classification. Therefore, even if a model is trained with well-formed learning data, it is difficult to instantly check where an error occurred if there is a failure. Although a technique to detect object type and location information by introducing classification and localization methods was attempted (Figure 1b), it could only identify the rough location, and many times it could not specify the location if many objects were mixed. Furthermore, since classification is designed to categorize one object at a time, it has to go through many filtering steps to identify many objects, which leads to increasing computation; thus, it would be difficult to apply in fields that require instant responses, such as waste categorization. For the above reasons, on-site classification is considered difficult when developing a construction waste model. Object detection is a method to detect several objects in an image and show the object's location with a bounding box (Figure 1c). It is possible to advance this method by increasing the intersection-over-union (IoU), which can generate more object proposals and distinguish object rotation, within-class diversity, and between-class similarity [50,51]. A function to differentiate several objects simultaneously was added to this method, making it suitable to classify waste; however, since it shows the location in a box shape, a human may find it difficult to assess if several objects are complexly mixed. In addition, the method aims to identify fixed set objects around human beings, making it inadequate to recognize atypical construction waste. Lastly, instance segmentation identifies several objects detected through object detection in the pixel unit according to the boundaries of each shape and indicates this by masking (Figure 1d). The masking effect makes this suitable for this study since construction waste that is tangled and overlapped can be identified, and the user can identify the object location and type intuitively. We designed a model to classify construction waste by restructuring the YOLACT model, with the fastest calculation among all instance segmentation models, using transfer learning. Firstly, the movement fundamentals for the previous instance segmentation are processed in a two-stage method that localizes objects in an image with a bounding box and outputs a predictive value using masking in accordance with the class quantity. However, instead of skipping localization, YOLACT executes the following two processes in parallel, which profoundly increase the calculation speed [17] and reduce computation: predicting the prototype-mask-dictionary forming process, which creates an object set to be closely examined in the image, and coefficients; linear combining with the prototype mask vector value; and generating an optimal mask per instance. As indicated in Figure 2, the comparison results of the two-stage instance segmentation model and YOLACT show that the algorithm processing speed of YOLACT, which predicts the mask in parallel in one-stage instance segmentation, was much faster.