# Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors
## 3 Material and Methods
### System Design
While subsequent iterations (YOLOv8, YOLOv10) have introduced incremental improvements, YOLOv5 continues to demonstrate robust performance across diverse operational scenarios [30]. Empirical evaluation by [31] indicates that YOLOv5 exhibits comparable or superior detection capabilities relative to newer variants in specific object detection tasks and general classification contexts. Among the available YOLOv5 variants (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x), YOLOv5m was ultimately selected for this project due to its optimal balance of performance characteristics [22]. Specifically, YOLOv5m demonstrates a favorable balance between precision and computational efficiency based on COCO benchmark evaluations. It provides higher accuracy than lightweight models such as YOLOv5s while requiring fewer computational resources than heavier models like YOLOv5l or YOLOv5x. Its CPU inference latency is also acceptable for real-time applications that demand efficient performance [30]. Additionally, YOLOv5 supports multiple export formats, such as ONNX, CoreML, and TFLite, facilitating deployment across various platforms [30]. For optimal real-time performance on mobile devices, the final implementation was converted to TFLite format, enabling rapid detection through direct camera streaming on standard mobile phones. Although art galleries typically feature fewer diverse obstacles compared to other public spaces, significant human traffic is common as visitors navigate through exhibition areas. Therefore, the system was trained to prioritize the detection of people as primary obstacles. In crowded situations, the system dynamically identifies and alerts based on the closest detected individual, ensuring relevant and timely obstacle notifications to users. Figure 3 illustrates an example of the decent service mentioned above, where the system detects the user's position and surrounding obstacles in real time and delivers relevant audio guidance accordingly. When a person enters the art gallery, the navigation guidance for viewing begins. For example, the system provides directional instructions such as \"You are at the entrance of Exhibition Room 1\" or \"Please proceed straight\". The system also continuously monitors the distance between individuals based on a predefined **Detection Threshold Distance**, which is established as a fixed value during system configuration. If a user approaches a stationary person and the distance falls within the **Detection Threshold Distance**, the system issues a warning to both parties. For the moving user, the system may announce, \"There is a person on your right side in the direction of movement\", while the stationary person may receive a warning such as, \"A person is approaching from your left side\". These alerts are delivered through both voice guidance and phone vibration to ensure effective communication. Additionally, while the user is navigating the gallery, the system provides contextual information about nearby artworks. For instance, it may announce, \"Artwork No. 1 is located to your left\". When the user turns toward the mentioned direction, the system automatically delivers information about the artwork. The level of detail in this information varies according to the user's movement speed: when the user is moving quickly, the system provides only basic information such as \"The title of this piece is XYZ, created by artist [PERSON]\", but when the user slows down or stops in front of an artwork, more comprehensive explanations including historical context, artistic technique, and significance are automatically provided. This ensures that the guidance service dynamically responds to the user's orientation, movement, and engagement level, facilitating an enhanced and interactive decent experience within the art gallery.