## 5 Experiments
### Test of the Proposed Method
#### 5.1.1 Overview of the Scenarios  
To demonstrate the method's applicability to different use cases, we propose four scenarios in UAV context (with Scenario I on MAV context, where MAV is a smaller, more lightweight type of UAV), each presenting different levels of computational requirements, power efficiency, and operational complexities. The rest of this part provides an overview of each scenario. Figure 4: Case study design. Scenario I: Low-Cost, Low-Power, Lightweight MAV for Basic Object Detection Tasks  
This scenario targets the deployment of a cost-effective, lightweight Micro Air Vehicle (MAV) designed to perform basic object detection and person detection. Designed for affordability, agility, reliability, and energy efficiency, the MAV in this scenario is intended for straightforward applications in resource-constrained environments, such as environmental monitoring and basic navigation. The focus of this MAV design is to achieve a balance between low hardware requirements and functional adequacy for simple detection tasks. While lightweight construction and compact size are essential for agility of the system, energy efficiency is also important for extended operational time. Given the simplicity of its tasks, the system does not require high processing power. Instead, it requires moderate computational capacity to perform reliably without excessive energy consumption. Additionally, memory and storage requirements are minimal, as the MAV is not designed for large-scale data handling or complex model processing. Scenario II: Mid-Level UAV for AI-Based Obstacle Detection  
Scenario II focuses on the design of a UAV intended for applications requiring moderate computational performance, suitable for AI-based obstacle detection. In this scenario, the UAV needs to perform more complex tasks than basic detection algorithms, such as real-time obstacle avoidance, but without significantly increasing cost. While power consumption and reliability are still considered, they are not the primary priorities. This suggests that the UAV is intended for environments where occasional trade-offs in energy efficiency and reliability can be tolerated in exchange for the added computational capabilities needed for AI-based processing. Additionally, weight and size are secondary considerations, suggesting that the UAV can accommodate slightly bulkier or heavier components to support the increased processing power required for these AI tasks. Scenario III: General-Purpose UAV for Outdoor Object Detection  
This scenario targets the design of a cost-effective UAV intended for general-purpose object detection in outdoor environments such as forests, agricultural lands, or urban areas. The highest priority is on affordability, with processing power being a secondary concern, sufficient to handle moderate detection tasks. The UAV requires basic resources such as flash memory and RAM to store necessary data and run lightweight detection algorithms effectively. While power consumption is considered, it is not a primary focus, as the UAV is designed for tasks that do not demand extensive power resources. Reliability is also not prioritized, enabling the use of more affordable components that fulfill the functional requirements without high costs. Weight and size are less critical in this design, but should still be managed to ensure adequate operational efficiency in outdoor settings. This UAV is intended for organizations seeking a budget-friendly, straightforward solution for outdoor object detection tasks that do not require extensive computational demands. Scenario IV: High-Performance UAV for Real-Time Advanced Object Detection  
and Tracking  
In this scenario, the UAV is tasked with performing real-time 3D object detection for autonomous navigation. The UAV operates in a dynamic environment, requiring high computational power for fast and accurate inference to identify objects, obstacles, and terrain in real-time.