# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 3 System Architecture, Design, and Implementation
### Enhancing Driving Safety by Forward Obstacle Detection  
Object detection models can be broadly categorized into one-stage and two-stage detection methods. Two-stage methods, such as Faster Region-Based Convolutional Neural Network (Faster R-CNN), first propose regions where objects are likely located and then perform detailed detection within those regions. Although this approach achieves high accuracy, its computational complexity and multi-step processing make it unsuitable for real-time applications. In contrast, one-stage detection methods process an entire image in a single computational step while simultaneously predicting object locations and classifications, providing a significant speed advantage. Prominent one-stage models include the Single Shot Multi-box Detector (SSD), EfficientDet, RetinaNet, and You Only Look Once (YOLO) models. Figure 4: Enhancing safety of personal mobility vehicle strategy. Figure 3: Server and client (device) architecture. However, its limitations in detecting small objects pose challenges for forward obstacle detection, where identifying small and dynamic objects is essential. EfficientDet employs a Bi-directional Feature Pyramid Network (BiFPN) to enhance multi-scale information transfer and balance computational efficiency and detection performance. Although EfficientDet performs effectively in general-purpose applications, its capability in detecting small objects is inferior to that of YOLOv8, particularly in environments requiring high accuracy under diverse conditions. RetinaNet, equipped with Focal Loss to address class imbalance, excels in detecting objects of varying sizes. However, its slower processing speeds relative to other one-stage models make it less suitable for real-time applications, such as obstacle detection for electric wheelchairs, where rapid responses are crucial. In this paper, we utilized the existing YOLOv8 version as is. However, we exploited hardware acceleration techniques to boost performance. Therefore, we reduced the overhead of inference operations by hardware acceleration of convolutional neural networks, supporting various layers such as convolution, deconvolution, pooling, batch normalization, and others. So, some components in the YOLO model, especially the colored components in Figure 5, were accelerated through the hardware in our experiment. As shown in Figures 5 and 6, the YOLOv8 architecture is composed of three key components: the backbone, neck, and detection layers, each optimized for efficient and accurate object detection. The backbone employs CSPDarknet53 as its backbone, incorporating the C2F module with dense and residual connections to enhance gradient flow and feature representation while maintaining a lightweight design. It downsamples the input image to retain the essential features and prepares it for subsequent stages. The Neck module employs a Feature Pyramid Network (FPN) to apply upsampling to enhance small object detection by creating a multi-scale feature pyramid, resulting in hierarchical feature fusion, enabling robust detection across objects of varying scales. Finally, the detection head replaces traditional anchors with a task-aligned assi  
Figure 5: YOLOv8 and its accelerating architecture exploited on this research. and robustness by better aligning predictions with ground truths. It outputs bounding boxes, class probabilities, and confidence scores, refining accuracy through loss functions. The Backbone-Neck-Head structure enables YOLOv8 to achieve multi-scale detection at high speeds, making it suitable for real-time applications such as obstacle detection in electric wheelchairs. YOLOv8 was selected as the optimal model for this study due to its exceptional combination of speed and accuracy. The innovative architecture of YOLOv8--comprising Backbone, Neck, and Head modules--enables rapid and precise detection by simultaneously predicting bounding boxes and class probabilities.