title: ÐÐ½ÑÑÑÑÐ¼ÐµÐ½ÑÑ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð¿ÐµÑÐ°ÑÐ½ÑÑ Ð¿Ð»Ð°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: PCB defect segmentation tools
Creators: name: ÐÐ¸Ð½ÑÐºÐ¾Ð²Ð°, ÐÐºÐ°ÑÐµÑÐ¸Ð½Ð°
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ÑÐ½Ð´Ð°Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð·Ð°Ð´Ð°ÑÐ° ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸
SAM
YOLO
foundation models
neural networks
computer vision
segmentation problem

description: Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÑÂ  ÑÑÐ½Ð´Ð°Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð² ÑÐ¿ÐµÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½ÑÑ Ð¾Ð±Ð»Ð°ÑÑÑÑ Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐµÐ½Ð½ÑÑ ÑÐµÑÑÑÑÐ¾Ð². Ð¢Ð°ÐºÐ¶Ðµ Ð² Ð¿ÑÐ¾ÑÐµcÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ´ÐµÑ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¾ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð¾ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ½Ð¾Ðµ ÑÐµÑÐµÐ½Ð¸Ðµ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ðµ Ð½Ð° ÑÑÐ½Ð´Ð°Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÑÑ Ð´Ð»Ñ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð¹ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: A. ÐÐ½Ð°Ð»Ð¸Ð· Ð»Ð¸ÑÐµÑÐ°ÑÑÑÑ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ B. ÐÐ¿Ð¸ÑÐ°Ð½Ð¸Ðµ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ C. ÐÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ D. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐ¸ÑÐ»ÐµÐ½Ð½ÑÑ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ð Ð¿ÑÐ¾ÑÐµÑÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¸Ð·ÑÑÐµÐ½Ð° ÑÑÐ¶ÐµÐ»Ð¾Ð²ÐµÑÐ½Ð°Ñ ÑÑÐ½Ð´Ð°Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¿Ð¾Ð¼Ð¾Ð³Ð°ÐµÑ ÑÐµÑÐ°ÑÑ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð±Ð°Ð·Ð¸ÑÑÑÑÐ°ÑÑÑ Ð½Ð° Ð½ÐµÐ¹ Ð±Ð¾Ð»ÐµÐµ Ð»ÐµÐ³ÐºÐ¾Ð²ÐµÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð² Ð¿Ð¾ÑÐ»ÐµÐ´ÑÑÑÐµÐ¼ Ð±ÑÐ´ÐµÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð² Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ðµ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð¾ ÑÐµÑÐµÐ½Ð¸Ðµ, Ð²ÐºÐ»ÑÑÐ°ÑÑÐµÐµ Ð² ÑÐµÐ±Ñ ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ñ, Ð±Ð°Ð·Ð¸ÑÑÑÑÑÑÑÑ Ð½Ð° ÑÑÐ½Ð´Ð°Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ð¹, ÑÐ¿Ð¾ÑÐ¾Ð±Ð½ÑÑ ÑÐµÑÐ°ÑÑ Ð·Ð°Ð´Ð°ÑÑ ÑÐ¿ÐµÑÐ¸ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð±ÐµÐ· Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐ°Ð¼Ð¾Ð¹ ÑÑÐ½Ð´Ð°Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð°Ð¿ÑÑÐ¼ÑÑ. ÐÑÐ» Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·, Ð² ÐºÐ¾ÑÐ¾ÑÐ¾Ð¼ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð»Ð¸ÑÑ Ð¼ÐµÐ¶Ð´Ñ ÑÐ¾Ð±Ð¾Ð¹ Ð´Ð²Ð° ÑÐµÑÐµÐ½Ð¸Ñ: Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÐ¹ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼, Ð¸ ÑÐ¿ÐµÑÐ¸Ð°Ð»ÑÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð¿Ð¾Ð´ Ð´Ð°Ð½Ð½ÑÑ Ð·Ð°Ð´Ð°ÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. Ð¡ÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð¸ÑÑÐ¾Ð´Ð¸Ð»Ð¾ Ð¿Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐ¼ Ð¼ÐµÑÑÐ¸ÐºÐ°Ð¼, Ð·Ð°Ð½Ð¸Ð¼Ð°ÐµÐ¼Ð¾Ð¹ Ð¿Ð°Ð¼ÑÑÐ¸, Ð° ÑÐ°ÐºÐ¶Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐ°Ð±Ð¾ÑÑ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð´Ð»Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾Ð´ ÑÐ¿ÐµÑÐ¸ÑÐ¸ÐºÑ Ð´ÑÑÐ³Ð¸Ñ Ð¾Ð±Ð»Ð°ÑÑÐµÐ¹.
description: The purpose of this work is to investigate the possibility of using a fundamental neural network model to solve the problem of segmentation in specific subject areas in conditions of limited resources. Also, in the process of work, an architectural solution based on fundamental models for the proposed subject area will be proposed and implemented. Tasks that were solved during the research: A. Literature analysis for solving the segmentation problem B. Description of the proposed solution C. Data preprocessing and analysis D. Conducting numerical experiments In the process of work, a heavyweight fundamental model was studied, which helps to solve computer vision problems, as well as a more lightweight model based on it, which will later be used in the proposed algorithm. As a result of the work, a solution was developed that includes a universal model based on the fundamental one, capable of solving the problem of a specific subject area without additional training of the fundamental model itself directly. A comparative analysis was performed, in which two solutions were compared: the algorithm proposed in this paper, and a convolutional model of a neural network specially trained for this task. The comparison took place according to the presented metrics, occupied memory, as well as training and work time. The results obtained can be used for further analysis and improvement of the proposed solution for the specifics of other areas.