title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð»Ð¾ÐºÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¾Ð² Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ñ Ð¸ Ð·Ð°Ð´ÑÐ¼Ð»ÐµÐ½Ð¸Ñ 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: Application of the neural network segmentation method for localization of sources of ignition and smoke in aerial photographs
Creators: name: Ð¡Ð¾Ð»Ð¾Ð´Ð¾Ð²Ð½Ð¸ÐºÐ¾Ð², ÐÐ²Ð°Ð½
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: 2024
Subjects: ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ
ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ
segmentation
deep learning
convolutional neural network
classical computer vision algorithms
data set development

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â Ð¸ÑÑÐ¾ÑÐ½Ð¸ÐºÐ¸ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ñ Ð¸ Ð·Ð°Ð´ÑÐ¼Ð»ÐµÐ½Ð¸Ñ Ð½Ð° Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ°Ñ. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ð¹ Ð¸ Ð·Ð°Ð´ÑÐ¼Ð»ÐµÐ½Ð¸Ð¹ Ð½Ð° Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ°Ñ. Ð ÑÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÐµ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²Ð¾Ð·Ð³Ð¾ÑÐ°Ð½Ð¸Ð¹ Ð¸ Ð·Ð°Ð´ÑÐ¼Ð»ÐµÐ½Ð¸Ð¹. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ñ ÑÐµÑÐ°ÑÑ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ð¸ Ð²Ð¸Ð´ÐµÐ¾. Ð ÑÐ°Ð¼ÐºÐ°Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ â YOLOv8-seg. Ð ÑÐ¾Ð´Ðµ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð° Ð¼ÐµÑÑÐ¸ÐºÐ° mAP50 Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ²Ð¸Ð´ÐµÑÐµÐ»ÑÑÑÐ²Ð¾Ð²Ð°Ð»Ð¸ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¾Ð³Ð½Ñ Ð½Ð° ÑÐµÑÑÐ¾Ð²ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ð´Ð¾ÑÑÐ¸Ð³Ð»Ð° 90,3%, Ð² ÑÐ¾ Ð²ÑÐµÐ¼Ñ ÐºÐ°Ðº ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð´ÑÐ¼Ð° ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 58,5%. ÐÐ±ÑÐ°Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð´Ð»Ñ Ð¾Ð±Ð¾Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¾Ð² ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð° 74,4%. ÐÐ° Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¾Ð¼ Ð¿ÑÐ¾ÑÐµÑÑÐ¾ÑÐµ NVIDIA Tesla T4 Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð»Ð° ÑÐ°ÑÑÐ¾ÑÑ ÐºÐ°Ð´ÑÐ¾Ð² 48 FPS. ÐÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð¹ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ñ NVIDIA Jetson Nano ÑÐºÐ¾ÑÐ¾ÑÑÑ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐ»Ð° 15 ÐºÐ°Ð´ÑÐ¾Ð² Ð² ÑÐµÐºÑÐ½Ð´Ñ, ÑÑÐ¾ ÑÐ²Ð»ÑÐµÑÑÑ Ð²Ð¿Ð¾Ð»Ð½Ðµ Ð¿ÑÐ¸ÐµÐ¼Ð»ÐµÐ¼ÑÐ¼ Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð±ÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ YOLOv8-seg Ð±ÑÐ´ÐµÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð½Ð° Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð¹ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ðµ, ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð½Ð° ÐÐÐÐ. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¾ÑÐºÑÑÑÑÐµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÑÑÑÑ Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¿Ð¾Ð¸ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸. ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÑÐµÐ´ÑÑÐ²Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ (Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹) ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Python. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¾ (Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¾) Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ Python.
description: The object of the study is the sources of ignition and smoke in aerial photographs.The purpose of the work is to develop and study a segmentation algorithm for detecting fires and smoke in aerial photographs.This study analyzes computer vision techniques used to detect fires and smoke. Deep learning methods that can solve the problems of searching and segmenting objects in images and videos are considered. As part of the study, a realâtime segmentation model, YOLOv8-seg, was selected. During the study, the mAP50 metric was used to evaluate the effectiveness of segmentation and detection processes. The results showed that the accuracy of fire segmentation in the test images reached 90.3%, while the accuracy of smoke segmentation was 58.5%. The overall accuracy for both classes was 74.4%. On the NVIDIA Tesla T4 GPU, the model demonstrated a frame rate of 48 FPS. When using the NVIDIA Jetson Nano mobile platform, the speed was 15 frames per second, which is quite acceptable for the segmentation task. Based on the data obtained, it was concluded that YOLOv8-seg will work effectively on the mobile platform installed on the UAV. Open educational resources and information search and analysis programs were used. Automation (automated) development tools such as Python was used. The Python software has been applied (tested).