BGA-YOLOX-s：基于鬼卷积模块和联合多尺度融合注意机制的蚕茧缺陷实时细粒度检测

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-12-04 DOI:10.1016/j.chemolab.2024.105294

Qingping Mei , Wujin Jiang , Kunpeng Mao , Yunchao Ding , Yuanli Hu

{"title":"BGA-YOLOX-s：基于鬼卷积模块和联合多尺度融合注意机制的蚕茧缺陷实时细粒度检测","authors":"Qingping Mei , Wujin Jiang , Kunpeng Mao , Yunchao Ding , Yuanli Hu","doi":"10.1016/j.chemolab.2024.105294","DOIUrl":null,"url":null,"abstract":"<div><div>The study addresses deficiencies in silkworm cocoon defect detection, enhancing the YOLOX-s network with the BGA-YOLOX-s model. By incorporating BiFPN-m, it reduces feature information loss, improving model reasoning speed. Ghost convolution reduces complexity and parameters, decreasing computational expenses. An attention module (CA) enhances fine-grained feature extraction. Experimental results on a cocoon dataset reveal a 4.1 % accuracy boost to 94.89 % compared to YOLOX-s. Furthermore, BGA-YOLOX-s outperforms SSD, YOLOv3, YOLOv4, and YOLOv5 in defect detection. The model proves effective in online cocoon defect detection, offering guidance for future applications in the production process.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105294"},"PeriodicalIF":3.7000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BGA-YOLOX-s: Real-time fine-grained detection of silkworm cocoon defects with a ghost convolution module and a joint multiscale fusion attention mechanism\",\"authors\":\"Qingping Mei , Wujin Jiang , Kunpeng Mao , Yunchao Ding , Yuanli Hu\",\"doi\":\"10.1016/j.chemolab.2024.105294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The study addresses deficiencies in silkworm cocoon defect detection, enhancing the YOLOX-s network with the BGA-YOLOX-s model. By incorporating BiFPN-m, it reduces feature information loss, improving model reasoning speed. Ghost convolution reduces complexity and parameters, decreasing computational expenses. An attention module (CA) enhances fine-grained feature extraction. Experimental results on a cocoon dataset reveal a 4.1 % accuracy boost to 94.89 % compared to YOLOX-s. Furthermore, BGA-YOLOX-s outperforms SSD, YOLOv3, YOLOv4, and YOLOv5 in defect detection. The model proves effective in online cocoon defect detection, offering guidance for future applications in the production process.</div></div>\",\"PeriodicalId\":9774,\"journal\":{\"name\":\"Chemometrics and Intelligent Laboratory Systems\",\"volume\":\"257 \",\"pages\":\"Article 105294\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemometrics and Intelligent Laboratory Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S016974392400234X\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemometrics and Intelligent Laboratory Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016974392400234X","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

本研究针对蚕茧缺陷检测的不足，利用BGA-YOLOX-s模型增强了YOLOX-s网络。通过引入BiFPN-m，减少了特征信息的丢失，提高了模型的推理速度。幽灵卷积减少了复杂性和参数，减少了计算费用。注意模块（CA）增强了细粒度特征提取。在茧数据集上的实验结果显示，与YOLOX-s相比，准确率提高了4.1%，达到94.89%。此外，BGA-YOLOX-s在缺陷检测方面优于SSD、YOLOv3、YOLOv4和YOLOv5。结果表明，该模型在蚕茧缺陷在线检测中是有效的，为今后在生产过程中的应用提供了指导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

BGA-YOLOX-s: Real-time fine-grained detection of silkworm cocoon defects with a ghost convolution module and a joint multiscale fusion attention mechanism

The study addresses deficiencies in silkworm cocoon defect detection, enhancing the YOLOX-s network with the BGA-YOLOX-s model. By incorporating BiFPN-m, it reduces feature information loss, improving model reasoning speed. Ghost convolution reduces complexity and parameters, decreasing computational expenses. An attention module (CA) enhances fine-grained feature extraction. Experimental results on a cocoon dataset reveal a 4.1 % accuracy boost to 94.89 % compared to YOLOX-s. Furthermore, BGA-YOLOX-s outperforms SSD, YOLOv3, YOLOv4, and YOLOv5 in defect detection. The model proves effective in online cocoon defect detection, offering guidance for future applications in the production process.

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

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.