title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ð¹ Ð¿Ð¾ Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a convolution neural network based fire detection system for video streams
Creators: name: ÐÐ¸Ð°Ð±Ð¸, Ð£Ð¼Ð°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: ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ð¹
Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾Ðº
ÑÐ²ÐµÑÑÐºÐ°
depthwise separable convolution
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
fire recognition
videostream
convolution
neural networks
image precessing

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ð¹ Ð¿Ð¾ Ð²Ð¸Ð´ÐµÐ¾Ð¿Ð¾ÑÐ¾ÐºÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð Ð°Ð±Ð¾ÑÐ° ÑÐ¾Ð´ÐµÑÐ¶Ð¸Ñ Ð¾Ð±Ð·Ð¾Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ, Ð¾ÑÐ½Ð¾ÑÑÑÐ¸ÑÑÑ Ðº Ð·Ð°Ð´Ð°ÑÐ°Ð¼ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð², Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð·Ð°Ð´Ð°Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ð¹.

ÐÑÐ½Ð¾Ð²Ð½Ð¾Ð¹ ÑÐ¿Ð¾Ñ ÑÐ´ÐµÐ»Ð°Ð½ Ð½Ð° ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð°Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ Ð´Ð»Ñ Ð¿Ð¾Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ð¸ÑÑÐ¾ÑÐ¸Ð¸ ÑÑÐ¾Ð³Ð¾ ÑÐ¸Ð¿Ð° ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¾Ð² Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð½ÐµÐºÐ¾ÑÐ¾ÑÑÑ Ð¸Ð· Ð½Ð¸Ñ Ð² Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐµ. ÐÐ°Ð»ÐµÐµ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð´Ð»Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸.

ÐÑÐ¸Ð³Ð¸Ð½Ð°Ð»ÑÐ½Ð¾ÑÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð²Ð¾-Ð¿ÐµÑÐ²ÑÑ, Ð¾Ð±ÑÑÐ°ÑÑÐµÐ¹ÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð½ÐµÐ±Ð¾Ð»ÑÑÐ¾Ð¼ Ð¾Ð±ÑÐµÐ¼Ðµ Ð´Ð°Ð½Ð½ÑÑ, Ð²Ð¾-Ð²ÑÐ¾ÑÑÑ, ÑÑÐµÐ±ÑÑÑÐµÐ¹ Ð¼ÐµÐ½ÑÑÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¼Ð¾ÑÐ½Ð¾ÑÑÐµÐ¹ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð´ÑÑÐ³Ð¸Ð¼Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÑÐ¼Ð¸.

ÐÐ»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ ÑÐ¸Ð¿ ÑÐ²ÐµÑÑÐºÐ¸, Ð½Ð°Ð·ÑÐ²Ð°ÐµÐ¼ÑÐ¹ "ÑÐ²ÐµÑÑÐºÐ° Ð¿Ð¾ Ð³Ð»ÑÐ±Ð¸Ð½Ðµ" (Ð°Ð½Ð³Ð». Depthwise separable convolution). ÐÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÑ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¸Ð¿Ð° ÑÐ²ÐµÑÑÐºÐ¸ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ° ÑÐ²ÐµÑÑÐºÐ¸ Ð½Ð° Ð´Ð²Ðµ ÑÐ°ÑÑÐ¸: depthwise convolution Ð¸ pointwise convolution.

ÐÐ»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±ÑÐ» Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½ Ð´Ð°ÑÐ°ÑÐµÑ Ð¸Ð· 3040 Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. ÐÐ¾ÑÐºÐ¾Ð»ÑÐºÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð¾Ð±ÑÑÐ½Ð¾ Ð¾Ð±ÑÑÐ°ÑÑÑÑ Ð½Ð° Ð±Ð¾Ð»ÑÑÐ¸Ñ Ð´Ð°ÑÐ°ÑÐµÑÐ°Ñ, Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½ÑÑ, ÑÑÐ¾Ð±Ñ Ð¿Ð¾Ð²ÑÑÐ¸ÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸.

Ð¢Ð°ÐºÐ¶Ðµ Ð² ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, Ð¾Ð½Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑÑÑ Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼Ð¸ Ð´ÑÑÐ³Ð¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð². ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÑÐ¾ÑÐ¼ÑÐ»Ð¸ÑÑÑÑÑÑ ÑÐ¿Ð¾ÑÐ¾Ð±Ñ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸.
description: The goal of this thesis is to develop a method for automatic detection of fire from a video 
stream based on convolutional neural networks. Since a video is a sequence of images, in order to make predictions for a given video stream , we make a prediction for each image that is part of the video stream. We first explore the existing methods and algorithms in the field of computer vision related to object recognition problems, and then we move on to study specific architectures of deep neural networks for fire recognition tasks.

More specifically, we start with a modern overview of machine learning methods and convolutional neural networks to understand the history of this type of statistical models, then we study the disadvantages of applying some of those methods in our current task, and then we propose our architecture for an effective solution of our given task.

The originality of our work lies in the fact that we focus on developing a model of convolutional neural networks that is trained on a relatively small amount of data, on the one hand, and requires less computing power compared to other models on the other hand. 

To achieve this goal, we use a type of convolution called depthwise separate convolution. The particularity of this type of convolution is that it divides the convolution process into two parts: the first one is called depthwise convolution and the second one pointwise convolution.

And for the training part of our model, we managed to build a small dataset of approximately 3040 images from Google images. 
Since neural networks are usually trained on large datasets, we use some data augmentation techniques to help our model more effectively generalize on new invisible data.

Finally, we present the results of our algorithm, and then compare these results with the results of other approaches, and then we will suggest some ways to improve our model in the future.