title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐµÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¼ÑÐ»ÑÑÐ¸ÐºÐ¾Ð¿ÑÐµÑÐ¾Ð²
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: Development of a software real-time system for recognition of multicopters
Creators: name: ÐÐ¾ÑÐ¾Ð², 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: 2022
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸
Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ðµ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
deep learning
real-time recognition
image
image processing algorithms
computer vision

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐµÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¼ÑÐ»ÑÑÐ¸ÐºÐ¾Ð¿ÑÐµÑÐ¾Ð².Â ÐÐ»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐ¸ Ð±ÑÐ»Ð¾ ÑÐµÑÐµÐ½Ð¾ ÑÐ¾Ð·Ð´Ð°ÑÑ ÑÐ¸ÑÑÐµÐ¼Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ CUDA, CUDNN; Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº OPENCV, OPENMP, darknet; Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸ Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ¾Ð¹ YOLO Ð¸ Ð¾Ð±ÑÑÐ°ÑÑÐµÐ¹ Ð²ÑÐ±Ð¾ÑÐºÐ¸ Ñ Ð±Ð¾Ð»ÐµÐµ 2000 Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹.Â Ð ÑÐ¸ÑÑÐµÐ¼Ðµ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑÑÑ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ - Ð½Ð°Ð´ Ð²ÑÐ¾Ð´Ð½ÑÐ¼ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÐµÐ¼ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑÑÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð½ÑÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸ (Ð²ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð³Ð¸ÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼). ÐÐ¾ÑÐ»Ðµ ÑÑÐ¾Ð³Ð¾ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÐµÑ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÑ Ð¿Ð¾Ð»ÑÑÐ¸Ð²ÑÐµÐ³Ð¾ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ, ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð¼ ÑÐ²Ð»ÑÐµÑÑÑ Ð¼Ð°ÑÑÐ¸ÑÐ°, ÑÐ¾ÑÑÐ¾ÑÑÐ°Ñ Ð¸Ð· ÐºÐ¾Ð¾ÑÐ´Ð¸Ð½Ð°Ñ Ð¿ÑÑÐ¼Ð¾ÑÐ³Ð¾Ð»ÑÐ½Ð¸ÐºÐ¾Ð² Ð¸ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐµÐ¹ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð² ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐµÐ¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¸ÑÐºÐ¾Ð¼Ð¾Ð³Ð¾ Ð¾Ð±ÑÐµÐºÑÐ°. ÐÐ°Ð»ÐµÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÐµÑÑÑ Ð¿Ð¾ÑÑÐ¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° â Ð¾ÑÐ±ÑÐ°ÑÑÐ²Ð°ÑÑÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑÑ Ð½Ð¸Ð¶Ðµ Ð¿Ð¾ÑÐ¾Ð³Ð¾Ð²Ð¾Ð¹, Ð²ÑÐ²Ð¾Ð´ÑÑÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ. ÐÐµÑÑ ÐºÐ¾Ð´ Ð½Ð°Ð¿Ð¸ÑÐ°Ð½ Ð½Ð° C, C++.Â ÐÐ¾ ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ ÑÐ´Ð°Ð»Ð¾ÑÑ ÑÐ¾Ð·Ð´Ð°ÑÑ ÑÐ¸ÑÑÐµÐ¼Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¿Ð¾ÑÐ»Ðµ 4000 Ð¸ÑÐµÑÐ°ÑÐ¸Ð¹ Ð½Ð° Ð¾Ð±ÑÑÐ°ÑÑÐµÐ¹ Ð²ÑÐ±Ð¾ÑÐºÐµ (Ð¾Ð´Ð½Ð° Ð¸ÑÐµÑÐ°ÑÐ¸Ñ = 64 Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ) ÑÐ¼Ð¾Ð³Ð»Ð° Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð±Ð¾Ð»ÐµÐµ 80 %.Â Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ, Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¸ ÑÐ°Ð¹Ð» Ð²ÐµÑÐ¾Ð² Ð¼Ð¾Ð¶Ð½Ð¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÐ¸ÑÑÐµÐ¼ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¼ÑÐ»ÑÑÐ¸ÐºÐ¾Ð¿ÑÐµÑÐ¾Ð² Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð¼ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð² Ð²Ð¸Ð´Ðµ Ð´ÐµÐºÑÑÐ¾Ð¿Ð½Ð¾Ð³Ð¾ Ð¸Ð»Ð¸ Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ, Ð¾Ð±Ð»Ð°ÑÐ½Ð¾Ð³Ð¾ ÑÐµÑÐ²Ð¸ÑÐ° Ð¸Ð»Ð¸ Ð²ÑÑÑÐ°Ð¸Ð²Ð°ÐµÐ¼Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÑÐ¸ Ð²ÐµÑÐ° Ð¼Ð¾Ð¶Ð½Ð¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð´Ð»Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÑÐ¾Ð¹ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸ Ð½Ð° Ð´ÑÑÐ³Ð¾Ð¹ Ð²ÑÐ±Ð¾ÑÐºÐµ.
description: This work is devoted to the development of a real-time software system for recognizing multicopters.Â To solve this problem, it was decided to create a computer vision system using CUDA, CUDNN technology; OPENCV, OPENMP, darknet libraries; neural networks with YOLO architecture and training dataset with more than 2000 images.Â In the computer vision system, image preprocessing is performed - a contrast enhancement transformation (histogram equalization) is performed over the input image. After that, the neural network processes the resulting image, the result is a matrix consisting of the coordinates of the rectangles and the probabilities of finding the desired object in the corresponding area. Next, post-processing is performed - results with a probability below the threshold are discarded, the results are output. All the code is written in C, C++.Â In the course of the work, it was possible to create a computer vision system that, after 4,000 iterations on a training sample (one iteration = 64 images), was able to achieve detection accuracy of more than 80%.Â The results of this work, namely the architecture and the weights file, can be used to create multicopter detection systems in real time in the form of a desktop or mobile application, cloud service or embedded system, and these weights can also be used for further training of this neural network on another dataset.