ANALYTICAL REVIEW OF NEURAL NETWORK ALGORITHMS FOR FIRE DETECTION IN EMERGENCY SITUATIONS

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 detection tasks, which allows one to make an informed choice on the use of one or another algorithm.