用改进的 DeepLabv3+ 网络模型监测视频中的鸡蛋生长过程

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Fengyang Gu, Hui Zhu, Haiyang Wang, Yanbo Zhang, Fang Zuo, S. Ablameyko
{"title":"用改进的 DeepLabv3+ 网络模型监测视频中的鸡蛋生长过程","authors":"Fengyang Gu, Hui Zhu, Haiyang Wang, Yanbo Zhang, Fang Zuo, S. Ablameyko","doi":"10.1134/s1054661824700081","DOIUrl":null,"url":null,"abstract":"<p>The paper proposes the noninvasive image egg growing monitoring method based on an illumination and transfer learning. During the egg growing, the size of egg air cell is increased. The segmentation is performed to extract cells and segmentation parameters are adjusted and trained on an air cell datasets by transfer learning to separate air cells with high light transmittance from the background. The improved DeepLabV3+ network model for image egg monitoring is proposed. The network embeds coordinate attention in the lightweight network MobilenetV2. The decoder feature fusion method is improved to a semantic embedding branch structure. The middle-level features that have been newly introduced are merged with the high-level features and low-level features. The results show that the mean intersection over union of the model reaches 89.06% and that the mean pixel accuracy rate reaches 94.66%. The method can effectively segment the air cell part of the eggs. The feasibility of the method was verified by measuring the air cells of egg growing process from the 7th to the 19th day.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Monitoring of Egg Growing in Video by the Improved DeepLabv3+ Network Model\",\"authors\":\"Fengyang Gu, Hui Zhu, Haiyang Wang, Yanbo Zhang, Fang Zuo, S. Ablameyko\",\"doi\":\"10.1134/s1054661824700081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The paper proposes the noninvasive image egg growing monitoring method based on an illumination and transfer learning. During the egg growing, the size of egg air cell is increased. The segmentation is performed to extract cells and segmentation parameters are adjusted and trained on an air cell datasets by transfer learning to separate air cells with high light transmittance from the background. The improved DeepLabV3+ network model for image egg monitoring is proposed. The network embeds coordinate attention in the lightweight network MobilenetV2. The decoder feature fusion method is improved to a semantic embedding branch structure. The middle-level features that have been newly introduced are merged with the high-level features and low-level features. The results show that the mean intersection over union of the model reaches 89.06% and that the mean pixel accuracy rate reaches 94.66%. The method can effectively segment the air cell part of the eggs. The feasibility of the method was verified by measuring the air cells of egg growing process from the 7th to the 19th day.</p>\",\"PeriodicalId\":35400,\"journal\":{\"name\":\"PATTERN RECOGNITION AND IMAGE ANALYSIS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PATTERN RECOGNITION AND IMAGE ANALYSIS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1134/s1054661824700081\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PATTERN RECOGNITION AND IMAGE ANALYSIS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s1054661824700081","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了基于光照和迁移学习的无创图像鸡蛋生长监测方法。在鸡蛋生长过程中,鸡蛋气胞的尺寸会增大。为了从背景中分离出透光率高的气胞,需要对气胞进行分割提取,并通过迁移学习在气胞数据集上调整和训练分割参数。提出了用于图像卵监测的改进型 DeepLabV3+ 网络模型。该网络在轻量级网络 MobilenetV2 中嵌入了协调注意力。解码器特征融合方法改进为语义嵌入分支结构。新引入的中层特征与高层特征和低层特征进行了融合。结果表明,模型的平均交集超过结合率达到 89.06%,平均像素准确率达到 94.66%。该方法能有效分割鸡蛋的气胞部分。通过测量从第 7 天到第 19 天鸡蛋生长过程中的气胞,验证了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Monitoring of Egg Growing in Video by the Improved DeepLabv3+ Network Model

Monitoring of Egg Growing in Video by the Improved DeepLabv3+ Network Model

The paper proposes the noninvasive image egg growing monitoring method based on an illumination and transfer learning. During the egg growing, the size of egg air cell is increased. The segmentation is performed to extract cells and segmentation parameters are adjusted and trained on an air cell datasets by transfer learning to separate air cells with high light transmittance from the background. The improved DeepLabV3+ network model for image egg monitoring is proposed. The network embeds coordinate attention in the lightweight network MobilenetV2. The decoder feature fusion method is improved to a semantic embedding branch structure. The middle-level features that have been newly introduced are merged with the high-level features and low-level features. The results show that the mean intersection over union of the model reaches 89.06% and that the mean pixel accuracy rate reaches 94.66%. The method can effectively segment the air cell part of the eggs. The feasibility of the method was verified by measuring the air cells of egg growing process from the 7th to the 19th day.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
期刊介绍: The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信