# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Current Challenges
#### 5.1.2 Edge Computing Paradigm for UAV Real-Time Object Detection  
In this review, edge computing is the most commonly used paradigm for real-time detection, rather than cloud computing, as we illustrated in Figure 8. Cloud computing needs to upload large amounts of raw data to the cloud, which is a great challenge for network bandwidth. Although 5G can provide large bandwidth and low latency to realize quick transmission between the data generator end and the cloud center [124], in our investigation, using 5G technology is not the first choice for object detection based on UAVremote sensing applications. A possible reason for this is that the capacity of the network is measured by the bandwidth per cubic meter, and for UAV remote sensing applications, the number of 5G base stations and the number of connected devices in a large space may limit the performance of 5G [124]. Thus, despite some success with 5G in object detection that is not in the UAV remote sensing scenario, this problem still exists to some extent [125]. The odds of edge computing is that it allows for more functions to be deployed to end devices at the edge to enable them to process the generated data, thus solving computationally intensive offloading and latency problems [126]. There are two kinds of edge computing paradigms. One is to deploy a local edge server (ground station) for UAV information processing (Figure 7b), and the other one is to implement a UAV-onboard real-time embedded platform (Figure 7c). Undoubtedly, the ideal situation for UAV real-time object detection tasks is to perform onboard computation, which avoids the raw data transfer, which usually puts pressure on the transmission of the sensor-generated imagery data in object detection using computer vision. At the same time, although embedded edge computing platforms with GPU acceleration are available in the industry and have been validated in different UAV real-time detection scenarios, their computing power is still not comparable to that of professional-level computing platforms, which has led to a lot of research focusing on how to optimize algorithms [56; 65; 85], and most commonly, compress computer vision models that consist of deep neural networks. In [127], the authors pointed out that onboard processing could greatly increase the energy consumption of the UAV, making it impossible to take advantage of practical applications. In particular, the battery packs of commercial UAVs are for propulsion [128], and once this power is allocated to onboard processing and data transmission, the flight time will be significantly reduced, which will also lead to a decrease in the overall efficiency of the detection tasks [129]. Although an external battery can be chosen to power the computing platform exclusively, this in turn will increase the payload on the UAV and thus reduce the flight time as well. In this review, only 7 studies attempted to consider computing offloading. Computation offloading can significantly reduce energy consumption and computing resource requirements by a large margin, and this is a current area of research on mobile edge computing [130]. Some studies indicated that offloading processing is a feasible solution for real-time tasks, which takes into account energy consumption and processing speed [125; 127; 129]. There are four types of offloading types: binary offloading, partial offloading, hierarchical architectures, and distributed computing [126]. The offloading type depends on the UAV's flying time and the computational capacity of both the UAV-aided edge server and the ground edge server, and [131] pointed out that energy consumption and task completion time are the most crucial performance metrics for designing an offloading algorithm. The included literature in this review did not discuss the offloading technology applied in UAVs. One challenge mentioned by [132] indicated that the ground edge server may not have sufficient energy to execute an offloaded task.