# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 6 Conclusions  
This study reviewed the recent literature on real-time object detection based on UAV remote sensing. We investigated the advances in aerial real-time object detection and comprehensively considered different aspects, including application scenarios, hardware usage, algorithm deployment, and evaluation metrics. Accordingly, the current research status and challenges are presented, and a basis and suggestions for future research have been proposed.  
We have adopted a systematic literature review approach by first defining a concept map for real-time object detection by UAV remote sensing, and according to the concept map analysis, seven research questions were analyzed (RQs).  
The review findings well-illustrated seven research questions. More real-time object detection research exists in emergency rescue, precision agriculture, traffic, and public facility inspection scenarios (RQ1). Multi-rotor UAVs are the most-used platforms due to their ability to hover, thus providing easy access to remote sensing images. Although thermal and spectral sensors have emerged for studies, they currently need to become mainstream for real-time detection but are limited by the form and size of their image data. Thus, RGB images are being studied more in UAV remote sensing (RQ2). Most real-time detection adopts the edge computing paradigm, and the number of studies using the onboard processing strategy is much greater than those using the computation offloading strategy (RQ3). GPU-based edge computing platforms are widely used to complete real-time object detection in UAV remote sensing images. However, some applications also use CPU, VPU, TPU, and FPGA according to their requirements (RQ4). With the widespread use of deep learning, it has become the first choice for real-time object detection tasks (RQ5), so it is vital to optimize these algorithms to better deploy them on resource-constrained computing platforms. Optimization can be carried out, e.g., by the use of lightweight convolutional layers, by pruning parameters, utilizing low-rank factorization, and incorporating knowledge distillation (RQ6). Finally, for evaluation metrics, in addition to accuracy and speed, latency and energy deserve equal attention (RQ7).  
In conclusion, this paper has highlighted the challenges and key issues associated with implementing real-time object detection for UAV remote sensing. Through a comprehensive analysis of sensor usage, edge computing, and model compression, this paper has identified areas that require further research and development. Future research needs to explore real-time processing solutions for multi-spectral and hyper-spectral images and investigate the possibilities of hardware and algorithms under different edge computing paradigms. As mentioned in this paper, bridging multiple disciplines, such as robotics and remote sensing, is crucial to fostering innovation and progress in real-time UAV remote sensing, and collaborative research efforts that connect different fields of study can further leverage the latest technologies for the benefit of society.  
Conceptualization, Z.C., L.K., and J.V.; methodology, Z.C.; formal analysis, Z.C.; investigation, Z.C.; data curation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, Z.C., L.K., J.V., and W.W.; visualization, Z.C.; supervision, L.K., J.V., and W.W.; project administration, L.K, J.V., and W.W.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2021 ZD0110901) and the Science and Technology Planning Project of the Inner Mongolia Autonomous Region (2021 GG0341).  
Not applicable.  
The authors declare no conflicts of interest.