Advances in neural networks have enabled unmanned aerial vehicles (UAVs) to detect and recognize objects in real time, which has facilitated the use of UAVs autonomously in a variety of scenarios, including fire detection in emergency situations. The paper reviews a number of existing neural network-based detection algorithms, including convolutional neural networks, regional convolutional neural networks and their variants, deep neural networks with convolutional long short-term memory (ConvLSTM), methods integrating deep learning with correlation filtering through self-training, Siamese neural networks for target tracking, and the YOLO (You Only Look Once) family of algorithms. The main characteristics and differences between neural network algorithms are described, and a comparison of their performance in terms of mean average precision (mAP) and frame rate per second (FPS) is given. The conclusions of the article provide insight into the trade-offs between accuracy, speed and task-specific requirements in de
Author Biographies
Vasiliy Aleksandrovich Zorin, V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, Moscow
Researcher
Roman Valer'evich Meshcheryakov, V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences, Moscow
Dr. Sc. (Engineering), Professor of the Russian Academy of Sciences, Director of the Center for Intelligent Robotic Systems
