title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¶Ð°ÑÐ¸Ñ Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ¾Ð², ÑÐ°Ð·Ð´ÐµÐ»Ð¸Ð¼ÑÑ Ð½Ð° Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½ÑÐ¹ Ð¾Ð±ÑÐµÐºÑ Ð¸ ÑÐ¾Ð½
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: Analysis of the features of the implementation of the adaptive compression aerial photos algorithm separated into a dominant object and a background
Creators: name: ÐÐ¾Ð¹ÐºÐ¾Ð², ÐÐ°ÑÐ²ÐµÐ¹
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°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼Ð¾Ðº
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐ¶Ð°ÑÐ¸Ñ
YOLACT
JPEG
computer vision
aerial photo
segmentation
compression algorithm

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐ½Ð°Ð»Ð¸Ð· Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¶Ð°ÑÐ¸Ñ Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ¾Ð², ÑÐ°Ð·Ð´ÐµÐ»Ð¸Ð¼ÑÑ Ð½Ð° Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½ÑÐ¹ Ð¾Ð±ÑÐµÐºÑ Ð¸ ÑÐ¾Ð½Â». ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ Ð¿Ð¾ ÑÐºÐ·ÐµÐ¼Ð¿Ð»ÑÑÐ°Ð¼ Ð¸ ÑÐ¶Ð°ÑÐ¸Ðµ. ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¶Ð°ÑÐ¸Ñ Ð´Ð»Ñ ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐ¼Ð° Ð´Ð°Ð½Ð½ÑÑ Ð² Ð·Ð°Ð´Ð°ÑÐ°Ñ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ¾Ð² Ñ Ð»ÐµÑÐ°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ° Ð½Ð° Ð½Ð°Ð·ÐµÐ¼Ð½ÑÐµ Ð¿ÑÐ¸ÑÐ¼Ð½ÑÐµ ÑÑÐ°Ð½ÑÐ¸Ð¸ Ñ ÑÐµÐ»ÑÑ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¿ÐµÑÐµÐ´Ð°ÑÐ¸ Ð¿Ð¾ ÐºÐ°Ð½Ð°Ð»Ñ ÑÐ²ÑÐ·Ð¸. ÐÑÐ» Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´ Ð¾ ÑÐ¾Ð¼, ÑÑÐ¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð½Ðµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑ Ð¸Ð·-Ð·Ð° Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¾Ð², Ðº ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð¾ÑÐ½Ð¾ÑÑÑÑÑ Ð½ÐµÐ²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÑÐ²ÑÐ·Ð½Ð¾Ð¹ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½Ð¾Ð³Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð°, ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ Ð½ÐµÐ¿Ð¾ÑÑÐµÐ´ÑÑÐ²ÐµÐ½Ð½Ð¾Ð¼Ñ ÑÑÐ°ÑÑÐ¸Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»Ñ (Ð²Ð²Ð¾Ð´ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° ÐºÐ»Ð°ÑÑÐµÑÐ¾Ð², Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ° Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐ¸Ð²Ð°ÑÑÐ¸Ð¼ Ð¿ÑÑÐ¼Ð¾ÑÐ³Ð¾Ð»ÑÐ½Ð¸ÐºÐ¾Ð¼, ÑÑÑÐ°Ð½Ð¾Ð²ÐºÐ¸ Ð¿Ð¾ÑÐ¾Ð³Ð° Ð¸ Ð´Ñ.), Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ Ð·Ð°ÑÑÐ°ÑÑ Ð½Ð° Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÑ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ°. ÐÐ»Ñ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾Ð±Ð»Ð°ÑÑÐµÐ¹ Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½Ð¾Ð³Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð¸ ÑÐ¾Ð½Ð° Ð±ÑÐ»Ð° Ð²ÑÐ±ÑÐ°Ð½Ð° ÑÐ²ÑÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ YOLACT, Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¼ Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ OpenCV Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼, Ð¾ÑÑÑÐµÑÑÐ²Ð»ÑÑÑÐ¸Ð¹ JPEG-ÑÐ¶Ð°ÑÐ¸Ðµ Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÐµÐºÑÐ° Ð¸ ÑÐ¾Ð½Ð° Ñ ÑÐ¾ÑÑÐ°Ð½ÐµÐ½Ð¸ÐµÐ¼ 100% Ð¸ 1% ÐºÐ°ÑÐµÑÑÐ²Ð° ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÐµÐ½Ð½Ð¾. ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð±ÑÐ» Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐ¼ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾Ð¼ Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ñ Ð²Ð°ÑÑÐ¸ÑÑÐµÐ¼Ð¾Ð¹ Ð¿Ð»Ð¾ÑÐ°Ð´ÑÑ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð¿Ð»Ð¾ÑÐ°Ð´Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ, ÑÑÐ¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¶Ð°ÑÐ¸Ñ Ð² ÑÑÐµÐ´Ð½ÐµÐ¼ Ð¿Ð¾Ð²ÑÑÐ°ÐµÑ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½Ñ ÑÐ¶Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð±ÐµÐ· Ð¿Ð¾ÑÐµÑÐ¸ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð² 6,2 Ð¸Ð»Ð¸ 2,9 ÑÐ°Ð·, Ð² Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¾Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¸ Ð¿Ð»Ð¾ÑÐ°Ð´Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð² ÐºÐ°Ð´ÑÐµ, Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼Ð¸, Ðº ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð±ÑÐ» Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÑÐ¶Ð°ÑÐ¸Ñ JPEG Ñ ÑÐ¾ÑÑÐ°Ð½ÐµÐ½Ð¸ÐµÐ¼ 100% ÐºÐ°ÑÐµÑÑÐ²Ð° Ð´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÐµÐºÑÐ° Ð¸ ÑÐ¾Ð½Ð°.
description: The topic of the graduate qualification work is "Analysis of the features of the implementation of the adaptive compression aerial photos algorithm separated into a dominant object and a background." The subject of the graduate qualification work is instance segmentation and compression. This work is devoted to the implementation and application of an adaptive compression algorithm for reducing the size of data in the tasks of transmitting aerial photos from an aircraft to ground receiving stations in order to increase the transmission speed over a communication channel. A review and analysis of classical segmentation algorithms and deep learning methods was performed. It was concluded that classical algorithms are not suitable for solving segmentation problems due to disadvantages, which include the impossibility of providing a connected segmented region of a dominant signal, the requirement for direct user participation (entering the number of clusters, allocating an object with bounding box, setting a threshold, etc.), the time taken to process one aerial photo. The convolutional neural network YOLACT was chosen to separate the zones of the dominant signal and background, trained on the developed dataset. The algorithm was implemented on the Python programming language using the OpenCV library, it performs JPEG compression of the dominant object and background preserving 100% and 1% quality respectively. The algorithm was tested on images with a different number of dominant objects with a variable area relative to the image area. The results show that the adaptive compression algorithm, on the average, increases the data compression coefficient, without losing the quality of the presentation of dominant information, by 6.2 or 2.9 times, depending on the number and area of objects in the frame, compared to images a JPEG compression algorithm was applied to while maintaining 100% quality of the dominant object and background.