Key points are not available for this paper at this time.
This manuscript delves into the intricate realm of small target detection technology in Unmanned Aerial Vehicles (UAVs), a pivotal element in contemporary aviation dynamics, with widespread implications across military, civilian, and emergency response domains. Central to this is the detection of diminutive targets, a process entailing sophisticated sensors and image processing algorithms, contending with the intricacies of identifying small, non-distinct targets against multifaceted backgrounds. The paper conducts a comprehensive review of the primary challenges, technological breakthroughs, and a spectrum of algorithms including RNN, SSD, and YOLO, in addition to examining prominent techniques such as Faster R-CNN in UAV target discernment. Moreover, it accentuates the application of this technology in areas like military reconnaissance, agriculture, and disaster management, highlighting its promising potential in urban settings and within the Internet of Things framework. The discourse extends to prospective developments, emphasizing the surmounting of technical limitations through enhancements in algorithmic efficiency, integration of multi-sensor systems, and advancements in real-time processing capabilities. Empirical evaluations of the technology’s practical efficacy underscore its profound prospective impact. In summation, the technology of small target detection in UAVs, propelled by ceaseless innovation and pragmatic deployment, stands at the cusp of engendering substantial contributions across various sectors and in the evolution of smart urban concepts.
Junhong Yin (Mon,) studied this question.