## 3 Approach to Object Detection
### Preparation of the Dataset  
This section provides details of the datasets of small-sized electronic products used for training and testing the object detection model. First, the mobile battery was selected as the primary target of fire-causing items due to frequent involvement in fire incidents, as shown in Figure 1b. This study focuses on detecting fire-causing objects during the recycling process of used small-sized electronic products. As a result, products outside the target category may also appear, and these products also need to be collected to simulate the conditions of the recycling process. Therefore, non-target small-sized electronic products, including digital cameras, electronic toys, phones, etc., were also collected. Thirty-two mobile batteries and twenty-one other small-sized electronics (non-target products) were collected by purchasing and collecting products from online auctions, second-hand stores, and other channels, respectively, as shown in Figure 4a,b. After product collection, images of the products were collected under fixed photography conditions for object detection, as shown in Figure 5. The equipment used to capture the images included a conveyor, a USB camera with a zoom lens, and an LED dome illumination device. Dome illumination was setup to minimize the influence of external light sources, while the conveyor was prepared for real-time detection in subsequent processes. The conveyor belt also served as the background for photographing the objects. During the image collection process, the conveyor was not in operation. Instead, the small-sized electronic products were manually placed on the unmoving conveyor belt to capture the images. Regarding the acquisition of experimental equipment, the specifications of the equipment available may vary across different countries, and budget constraints also differ. Therefore, we have not discussed each piece of equipment in detail but have only listed  
Figure 4: Collected sample of collected small-sized electronic products: (**a**) mobile batteries; (**b**) other small-sized electronics. the devices used in the study. However, one point that needs to be emphasized is that, to prevent interference with object detection caused by unnecessary background, the camera's field of view must be restricted to the width of the conveyor belt. In this study, the conveyor belt width was 20 mm. In the image collection process, images were taken using predetermined combinations of small-sized electronics; since the number of potential combinations was too large, a limited conveyor belt size was necessary to streamline the number of combinations. Using mobile batteries as an example, the first project focused on detecting only one category: mobile batteries. Three combinations were implemented: one mobile battery, one mobile battery, one other small-sized electronics, and two mobile batteries. These combinations considered the rotation angle and front-back sides of the power banks. The collected images were labeled for categories and positions using the Image Labeler app in MATLAB. The mobile battery was marked with a green box, and an example of the labeled mobile batteries is shown in Figure 6.  
In the investigation of fire-causing items [3], aside from the mobile batteries, heated tobacco equipment (electronic cigarettes), cordless vacuum cleaners, and smartphones were the second, third, and sixth most frequent causes of fire incidents, respectively, as shown in Figure 1b. Due to the relatively large size of the cordless vacuum cleaner and the limited  
Figure 5: Experimental setup for photography and object detection. Figure 6: Example of labeled mobile batteries. size of the conveyor belt, the cordless vacuum cleaner was excluded as a detection target in this study. Therefore, in subsequent experiments, two additional categories, the heated tobacco and the smartphone, were added as detection targets alongside the original mobile battery dataset. Finally, 119 heated tobacco and 20 smartphones were collected, respectively, as shown in Figure 7a,b.