title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¿Ð¾Ð´ÑÑÐµÑÐ° Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð»ÑÐ½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² ÑÑÐµÐ´ÑÑÐ²Ð°Ð¼Ð¸ ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸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: Development of an algorithm for objects counting using computer vision
Creators: name: Ð¡Ð°Ð²Ð¸ÑÐºÐ¸Ð¹, 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: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ ÑÐµÑÐ¸
ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½Ð° Ð¾Ð´Ð½Ð¾Ð¼ Ð¿ÑÐ¸Ð¼ÐµÑÐµ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð¿Ð¾Ð´ÑÑÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð´Ð°ÑÐ°ÑÐµÑÑ
neural networks
convolutional networks
computer vision
one-shot learrning
object detection
object counting
datasets

description: ÐÐ±ÑÐµÐºÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÐµ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾Ð´Ð½Ð¾Ð¼ Ð¿ÑÐ¸Ð¼ÐµÑÐµ. Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð´Ð»Ñ Ð¿Ð¾Ð´ÑÑÑÑÐ° Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¸. ÐÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÑÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿Ð¾Ð´ÑÑÑÑÐ° Ð¸ Ð´ÐµÑÐµÐºÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð², Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° ÑÐ°Ð±Ð¾ÑÐ° Ñ Ð´Ð°ÑÐ°ÑÐµÑÐ°Ð¼Ð¸, Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¾ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½ÑÑ Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐµ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ ResNet 50, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð² Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ðµ Ð¿Ð¾Ð´ÑÑÑÑÐ°, Ð½Ð°Ð¿Ð¸ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð½Ð° ÑÐ·ÑÐºÐµ Python, Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°. ÐÑÐ»Ð¸ Ð´Ð¾ÑÑÐ¸Ð³Ð½ÑÑÑ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ Ð¼ÐµÑÑÐ¸Ðº: Ð´Ð»Ñ Ð¿Ð¾Ð»Ð½Ð¾ÑÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ ÑÐ¾ÑÑÐ°Ð²Ð¸Ð»Ð¾ 0.85, Ð´Ð»Ñ ÑÑÐµÐ´Ð½ÐµÐ¹ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ â 0.61. Ð Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ¼ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð³ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑÑÑ Ð´Ð»Ñ Ð´Ð¾ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, Ð¿ÐµÑÐµÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°ÑÐ°ÑÐµÑÐ° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ð¸ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿Ð¾Ð´ÑÑÑÑÐ° Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¿ÑÐµÐ´Ð¿ÑÐ¸ÑÑÐ¸ÑÑ Ð´Ð»Ñ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÑÐ°Ð½ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð´ÑÐºÑÐ¸Ð¸.
description: The object of the research is one-shot learning neural networks. The purpose of the work is to develop an algorithm using these models for object counting in images. An analysis of existing approaches to object counting and detection was conducted, datasets were processed, and several models based on the ResNet 50 convolutional neural network architecture were trained. These models are used for object detection within the counting algorithm, which is implemented in Python. An experimental study of this algorithm was also conducted. The following metric values were achieved: a recall of 0.85 and an average precision of 0.61. The results of this work can be used for further refinement of the developed algorithm, reworking the training dataset, and implementing the object counting system in enterprises for organizing logistics processes and optimizing product storage.