**2. Materials and Methods**

### *2.1. YOLOv5 Architecture*

Any robotic sorting system needs to accurately categorize recyclables from various waste material type. It is important for the system to develop an effective model. With the recent developments in deep learning, the YOLO family of models provides a scalable means for categorizing recyclables into various classes. Construction waste can be roughly split into 4 classes: brick, wood, stone and plastic, according to the material. Considering the accuracy and efficiency in object detection, the YOLOv5 model was chosen, which consists of three main architectural blocks: Backbone, Neck and Head, as shown in Figure 1.

**Figure 1.** Architecture of YOLOv5 Model.
