# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 2 Methodology
### Research Questions  
In order to address the problems of real-time object detection based on UAV remote sensing, the complete workflow of real-time object detection tasks in UAV remote sensing needs to be clearly demonstrated. Figure 1 summarizes the process of real-time object detection based on UAVs seen in previous studies [25; 26; 27] by drawing a concept map to illustrate each step.  
The complete workflow shown in Figure 1 includes five steps. The starting point comes from the real-time requirements for specific tasks in different application scenarios. Therefore, determining the targets and the task scenarios is critical for selecting UAVs, sensors, and algorithms. Then, during the data acquisition phase, the chosen UAVs are equipped with different sensors to complete data collection according to the task's needs. The remotely sensed data need to be processed in real time, so computing platform selection is essential for onboard processing. The chosen computing platforms need to establish the computing paradigm for implementing real-time object detection. Meanwhile, the core detection algorithms also need to be deployed on the platform in the object detection phase. The selection of communication methods determines how the data and results are transmitted in the workflow.  
Merely obtaining detection results holds limited significance in practical applications, as the essence of real-time detection lies in achieving rapid responsiveness. Hence, the outcomes of real-time object detection serve as crucial outputs for activities such as decision making, system alerts, or machinery operations. These outputs engender value and confer purpose upon this undertaking.  
The closed loop in Figure 1 can be used to describe the completed autonomous workflow of UAV real-time object detection. According to the research objectives, this review study will focus on advanced real-time object detection based on remote sensing technology, including application scenarios, hardware selection, algorithm design, and implementation methods. The research questions of this paper are correspondingly proposed as follows.  
RQ1. What are the application scenarios and tasks of real-time object detection based on UAV remote sensing?  
RQ2. What types of UAV platforms and sensors are used for different real-time object detection applications?  
RQ3. Which types of paradigms were used in real-time object detection based on UAV remote sensing?  
RQ4. What commonly used computing platforms can support the real-time detection of UAV remote sensing based on edge computing?  
RQ5. What algorithms were used for real-time detection based on UAV remote sensing?  
RQ6. Which improved methods were used in real-time detection algorithms for UAV remote sensing?  
RQ7. How can we evaluate real-time detection based on UAV remote sensing regarding accuracy, speed, and energy consumption?  
The formulation of these seven proposed RQs is based on the first four phases outlined in the concept map. It is essential to acknowledge that rapid response constitutes a pivotal stage within the entirety of the workflow. This stage serves as a subsequent action in handling the outputs of real-time detection. Consequently, the exploration of the final phase will be extended to delve into future prospects for attaining autonomous UAV capabilities, as elaborated on in Section 5.