## 3 Multiple Object Detection with YOLOv5
### YOLOv5 Training Data  
To detect situations occurring in the walking environment of visually impaired individuals, the training data combined publicly available image sets obtained from Roboflow [36] with images directly captured by us. By increasing the quantity of road and street images as seen from a pedestrian's eye level, we aim to enable the YOLOv5 model to learn better the objects of the defined four classes (bollards, crosswalks, pedestrian traffic light (red), and pedestrian traffic light (green)). The collection of public data is explained as follows. The classes were designed to focus on static objects that can be detected while walking. Because the shapes of bollards and signals vary by region, various forms of original images were collected through five Roboflow projects. We collected 3345, 2146, 679, 9497, and 1682 images from the following: the \"Traffic-light Computer Vision Project\" [37] and the \"Capstone for Detection1 Computer Vision Project\" [38], which includes walking environments such as signals, crosswalks, and bollards; the \"Traffic-sign Project\" [39], which includes various forms of bollards and crosswalks, depending on the region; the \"Pedestrian Signs and Lanes Computer Vision Project\" [40], which includes signals from various regions; and the \"July_6 Computer Vision Project\" [41], respectively. Images exceeding the permissible range of the object area and sharpness variations were removed, resulting in the construction of 5180 raw images. Of these, 25% of the raw images contained objects classified into two or more classes. Specifically, 1105 images contained objects classified into only two classes and 193 images contained objects classified into three classes, totaling 1298 images. To ensure sufficient images from the pedestrian's eye level, fifty-two images corresponding to the four classes were captured between 2 and 4 PM under clear and rainy or cloudy weather conditions. These images were augmented through blurring effects and noise application to triple the number and were refined for use in training. The device used to capture these images was an iPhone 15 Pro with a resolution of 4032 \(\times\) 3024. Annotation for creating the training data was conducted using RoboFlow. The four classes were selected considering obstacles encountered in real walking environments. The total number of annotations in the constructed training dataset was 9045. Figure 4 shows examples of collected images. Table 1 lists the specifications of the training data, including the number of images and annotations in each class. Figure 3: Multiple object detection pipeline. YOLOv5 recommends more than 1500 training images per class over 300 epochs [42]. Therefore, we used data augmentation techniques to expand the diversity of the training data from raw images. As presented in column 4 of Table 2, the number of annotations for each class is nearly equal. This demonstrates our ability to expand the training images using data augmentation techniques while simultaneously addressing the imbalance in the number of annotations per class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Number of Raw Images** & **Number of Annotations** &
\begin{tabular}{c} **Number of Annotation** \\ **after Augmentations** \\ \end{tabular} \\ \hline Bollard & 1187 & 3188 & 5659 \\ Crosswalk & 621 & 2484 & 5612 \\ Redlight & 1072 & 2005 & 5589 \\ Greenlight & 1002 & 1368 & 5482 \\ Bollard, Crosswalk & 363 & & \\ Bollard, Redlight & 5 & & \\ Bollard, Greenlight & 1 & & \\ Crosswalk, Redlight & 583 & & \\ Crosswalk, Greenlight & 149 & & \\ Redlight, Greenlight & 4 & & \\ Bollard, Crosswalk, Redlight & 154 & & \\ Bollard, Crosswalk, Greenlight & 37 & & \\ Bollard, Redlight, Greenlight & 1 & & \\ Crosswalk, Redlight, Greenlight & 0 & & \\ Total & 5180 & 9045 & 22,342 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of training data.