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Top1. Introduction
With the iterative update of electronic communication technology and the continuous maturity of cloud computing (Bhardwaj, et al., 2022; Kumar, et al., 2022), big data (Stergiou, et al., 2021), knowledge graph (Zhou, et al., 2023), sensors (Srivastava, et al., 2022), network security (Li, et al., 2022), edge computing (Lv, et al., 2022), and artificial intelligence technology (Wang, et al., 2020; Jelusic, et al., 2022), the UAV industry has entered a rapid development stage. Drones have been applied in various fields such as power detection, environmental protection, biological detection, logistics and transportation, disaster rescue, data collection, and mobile communication (Razakarivony., & Julie., 2016). In the coming years, the deep integration of drone technology with artificial intelligence (Li, D., et al., 2019; Nhi, et al., 2022), image processing (Chu, et al., 2022; Qian, et al., 2022; Zheng, et al., 2022), network security (Alomani, et al., 2022; Gaurav, et al., 2022) and other technologies will not only further overcome the problems of drones in current industrial production, it will also promote the landing of UAV applications in new fields (Betti., & Tucci., 2023; Ahmed, et al., 2022; Sun, et al., 2020; Luo, et al., 2022; Zhang, et al., 2021). The wide application of drones in society has significantly improved production efficiency and also considerably reduced the consumption of human, material, and financial resources. Drones are becoming increasingly important in today's society (Luo, et al., 2022).
Currently, deep learning-based (Sayour, et al., 2022; Kadry, et al., 2021) object detection algorithms can maintain high detection performance. For common scenes, such as those with a relatively single background, a small number of targets, a large target size, and a horizontal image shooting perspective, classical object detection algorithms can maintain high detection accuracy while ensuring detection speed (Fu, et al., 2021; Kim, et al., 2008; Betti., 2022). Aerial imagery is divided into satellite imagery captured by satellites and UAV imagery captured using UAVs; aerial imagery captured by satellites is characterized by large size, fixed shooting angle, and a small percentage of targets in the image (Huang. et al.,; Zhong, et al., 2017; Han, et al., 2019; Cao, et al., 2020; Elhagry, et al., 2022).
UAV images are more complex and richer because of the limitations of the shooting equipment, environment, and other factors. Compared with satellite images, UAV images are more widely used in civil and military fields, thus, exploring the potential information of UAV images is important for in-depth applications of UAVs in various fields (Zhang, et al., 2020; Dikbayir., & BÜLBÜL., 2020; Chen, et al., 2020). The application of UAVs in the civilian sector is relatively established, with the following specific application scenarios: