Revolutionizing automated pear picking using Mamba architecture.

IF 4.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Peirui Zhao, Weiwei Cai, Wenhua Zhou, Na Li
{"title":"Revolutionizing automated pear picking using Mamba architecture.","authors":"Peirui Zhao, Weiwei Cai, Wenhua Zhou, Na Li","doi":"10.1186/s13007-024-01287-z","DOIUrl":null,"url":null,"abstract":"<p><p>With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.</p>","PeriodicalId":20100,"journal":{"name":"Plant Methods","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536875/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Methods","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13007-024-01287-z","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

使用 Mamba 架构彻底改变梨的自动采摘。
随着新一代视觉架构 Vmamba 的出现,以及对农业产量和效率的进一步要求,我们提出了一种基于 Vmamba 的高效、高精度目标检测网络,用于自动采摘梨子的任务,旨在解决目前变压器架构效率低下的问题。该网络被命名为 SRSMamba,它采用奖惩机制 (RPM) 来关注重要信息,同时最大限度地减少冗余干扰。它利用三维选择性扫描(SS3D)来扩展扫描维度,并跨信道维度整合全局信息,从而增强了模型在复杂农业环境中的鲁棒性,并能有效适应梨园和农田中复杂地物的提取。此外,在特征融合阶段还引入了堆叠特征金字塔网络(SFPN)来增强语义信息,特别是提高了对小型目标的检测能力。实验结果表明,SRSMamba 的参数数较低,为 21.1 M,GFLOPs 为 50.4,mAP 为 72.0%,mAP50 为 94.8%,mAP75 为 68.1%,FPS 为 26.9。与其他最先进的(SOTA)物体检测方法相比,它在模型效率和检测精度之间实现了良好的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Plant Methods
Plant Methods 生物-植物科学
CiteScore
9.20
自引率
3.90%
发文量
121
审稿时长
2 months
期刊介绍: Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
×
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