# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 4 Results
### Two Paradigms for Real-Time Detection  
Real-time object detection means that the UAVs equipped with sensors and processors have the capacity to quickly process the collected data and reliably deliver the neededinformation. Thus, based on our review study, three kinds of paradigms are utilized for real-time object detection: embedded systems, cloud computing, and edge computing. The majority of real-time object detection studies (\(n=72\)) selected the edge computing paradigm, and 4 studies reported using the embedded system. At the same time, there was only one study that selected the cloud computing solution. Cloud computing is a computing paradigm that provides on-demand services to end-users through a pool of computing resources that includes storage, computing, data, applications, and so on [106]. The cloud computing paradigm is shown in Figure 7a. From our review, the advantages of the cloud computing paradigm are reflected in [73]. The first advantage is that the ground station stores the images collected via UAV and then streams them to the cloud. The data are processed on the cloud, which has powerful computing and storage resources for massive UAV remote sensing data. The second advantage is that the end-users from anywhere at any moment can access the cloud services. In addition, the fault-tolerance mechanism of cloud computing ensures the high reliability of its processing and analysis services, and the ability of the cloud center to dynamically allocate or release resources according to users' demand is also a strength. However, cloud computing has some drawbacks when dealing with real-time object detection tasks on UAVs. These drawbacks mainly stem from the growing volume of data at the edge, which is limited by network bandwidth and makes the data difficult to upload to the cloud for processing and analysis [107]. The emergence of edge computing can effectively solve the above problem. There are many definitions of edge computing [106; 107; 108]. However, the core of the description is that edge computing is the provision of cloud services and IT service environments for users at the edge of the network, with the goal of providing computing, storage, and network bandwidth services close to the end-devices or end-users, thereby reducing latency, ensuring efficient network operation and service delivery, and improving the user experience. For real-time UAV object detection, the edge end is usually the UAV that generates an enormous volume of data. Based on the above concept of edge computing, we can divide edge computing into two forms:  
1. One form where the edge end is unable to perform large computations; it just processes partial data and then offloades to the edge server (ground station) for processing. Figure 7: The paradigm of (**a**) cloud computing, (**b**) edge computing with remote processing, and (**c**) edge computing with onboard processing. Figure 7b shows the paradigm of edge computing in this form. In this case, the so-called ground station is a generic name and usually is a high-performance computing platform, such as a laptop [59], mobile phone [48; 51] or single-board [57]. 2. Another form where the edge end is also the edge server, which has the capability of processing data while collecting data. This paradigm can be seen in Figure 7c. There are many of these forms of edge computing in our review [31; 58; 84; 94], which integrated sensors and embedded computing platforms onto the UAV and treated them as a whole, which is the edge end. During the flight of the UAV, the images are captured and processed simultaneously to obtain the detection results. It is worth mentioning that some real-time object detection based on UAV remote sensing is one part of a complete system [31], so the results normally will upload to the cloud center, but the edge computing for real-time detection ends at the computing platform or ground station providing needed information. The subsequent tasks will not be discussed here.