title: Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð¶Ð°ÑÐ¾Ð² Ð½Ð° Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ°Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Implementation of aerial fire detection algorithm
Creators: name: ÐÐ»Ð¸ÐµÐ², ÐÐ»ÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Tiny-YOLOv3
Computer vision
detection
convolutional neural network
deep learning
classification

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description: The subject of the graduate qualification work is âImplementation of the fire detection algorithm for aerial imagesâ. This work is devoted to the consideration and implementation of various methods for the detection and localization of ignition sources by analyzing the video sequence obtained when shooting from high altitude. The real-time terrain monitoring algorithm from the camera mounted on the aircraft allows analyzing for the presence of a fire a vast area, and thus preventing the spread of fire. The aim of this work is to implement an algorithm for monitoring the terrain by the video stream received from the camera to detect fire and localize the source of ignition. Both classical methods of digital image processing and solutions based on deep learning technologies were considered. Among the methods of digital image processing, such approaches as finding wavelet transforms and optical flow, as well as applying filtering by brightness and hue, were studied in detail. After considering various modern approaches, the architecture of a convolutional neural network based on Tiny-YOLOv3 was chosen as the optimal method for detecting ignition sources on a video sequence. Neural network training was conducted on the developed training data set for two classes: fire and smoke. The data set was generated based on 44 videos, of which 1650 frames were obtained with the image of fires taken from the air in various conditions. As a result, the accuracy and speed of the network on a test data set were evaluated. The accuracy values were estimated using the average precision (AP) metric for various network parameters. For the size of the input layer of the Tiny-YOLOv3 network 416 Ã 416, the detection accuracy (AP) of fire and smoke was 56.5% and 31.9%, respectively. The mean average precision for the two classes was 48%; fire was detected in 89% of images containing fire. The neural network algorithm showed high speed (111 frames per second with an input layer size of 416 Ã 416).