• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

煤矸石识别图像与分选图像的高效匹配方法研究

Research on efficient matching method of coal gangue recognition image and sorting image.

作者信息

Ye Zhang, Hongwei Ma, Peng Wang, Wenjian Zhou, Xiangang Cao, Mingzhen Zhang

机构信息

School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.

Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi'an, 710054, China.

出版信息

Sci Rep. 2024 Oct 26;14(1):25536. doi: 10.1038/s41598-024-75654-0.

DOI:10.1038/s41598-024-75654-0
PMID:39461998
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11513082/
Abstract

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot's grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint's ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

摘要

当煤矸石分拣机器人分拣煤矸石时，由于带式输送机的皮带打滑、跑偏和速度波动，目标煤矸石的位置会发生变化。这将导致机器人抓取失败或抓空。我们针对此问题开发了一种解决方案：IMSSP-Net两阶段网络煤矸石图像快速匹配方法。该方法将重新获取目标矸石位置信息，提高机器人的抓取精度和效率。在第一阶段，我们使用SuperPoint来保证特征点提取的场景适应性和可信度。通过使用改进的带色彩恢复增强算法的多尺度视网膜算法，我们进一步增强了SuperPoint检测特征点的能力。在第二阶段，我们引入SuperGlue进行特征匹配，以提高匹配网络的鲁棒性。我们采用PROSAC算法消除错误的特征匹配点对，提高图像匹配的准确性。我们在不同物距、尺度、旋转角度和复杂条件下进行了图像匹配对比实验。实验平台采用团队自主研发的双机械手桁架式煤矸石分拣机器人。该方法的匹配精度、召回率和匹配时间分别为98.2%、98.3%和84.6毫秒。该方法能够满足煤矸石识别图像与分拣图像高效、准确匹配的要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/2cb4a76fc39a/41598_2024_75654_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/dcb8c8ec6d30/41598_2024_75654_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/f5670b1a4ca3/41598_2024_75654_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/eff3902091c3/41598_2024_75654_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/e1af1e5526dc/41598_2024_75654_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/dfb1c46794af/41598_2024_75654_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/dde8d9551fd5/41598_2024_75654_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/9fae9557d79e/41598_2024_75654_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/879a0eee5eb6/41598_2024_75654_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/16b4465dceb1/41598_2024_75654_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/1dd8405cd6f9/41598_2024_75654_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/f1fba3a6134d/41598_2024_75654_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/cb2e8e25754c/41598_2024_75654_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/615c24029be9/41598_2024_75654_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/65320dc16cd1/41598_2024_75654_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/e50c8ba4394e/41598_2024_75654_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/2cb4a76fc39a/41598_2024_75654_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/dcb8c8ec6d30/41598_2024_75654_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/f5670b1a4ca3/41598_2024_75654_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/eff3902091c3/41598_2024_75654_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/e1af1e5526dc/41598_2024_75654_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/dfb1c46794af/41598_2024_75654_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/dde8d9551fd5/41598_2024_75654_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/9fae9557d79e/41598_2024_75654_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/879a0eee5eb6/41598_2024_75654_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/16b4465dceb1/41598_2024_75654_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/1dd8405cd6f9/41598_2024_75654_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/f1fba3a6134d/41598_2024_75654_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/cb2e8e25754c/41598_2024_75654_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/615c24029be9/41598_2024_75654_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/65320dc16cd1/41598_2024_75654_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/e50c8ba4394e/41598_2024_75654_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0127/11513082/2cb4a76fc39a/41598_2024_75654_Fig16_HTML.jpg

相似文献

1
Research on efficient matching method of coal gangue recognition image and sorting image.煤矸石识别图像与分选图像的高效匹配方法研究
Sci Rep. 2024 Oct 26;14(1):25536. doi: 10.1038/s41598-024-75654-0.
2
Multi-Arm Global Cooperative Coal Gangue Sorting Method Based on Improved Hungarian Algorithm.基于改进的匈牙利算法的多臂全局协同煤矸石分拣方法。
Sensors (Basel). 2022 Oct 19;22(20):7987. doi: 10.3390/s22207987.
3
Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions.复杂条件下基于图像与卷积神经网络的煤矸石识别技术强泛化性研究
ACS Omega. 2023 Oct 13;8(43):40309-40320. doi: 10.1021/acsomega.3c04558. eCollection 2023 Oct 31.
4
Design and application of coal gangue sorting system based on deep learning.基于深度学习的煤矸石分选系统设计与应用
Sci Rep. 2024 Jul 17;14(1):16508. doi: 10.1038/s41598-024-67323-z.
5
Detection of Coal and Gangue Based on Improved YOLOv8.基于改进YOLOv8的煤与矸石检测
Sensors (Basel). 2024 Feb 15;24(4):1246. doi: 10.3390/s24041246.
6
Trajectory Planning for Coal Gangue Sorting Robot Tracking Fast-Mass Target under Multiple Constraints.多约束条件下煤矸石分拣机器人跟踪高速质量目标的轨迹规划。
Sensors (Basel). 2023 Apr 30;23(9):4412. doi: 10.3390/s23094412.
7
Coal and Gangue Separating Robot System Based on Computer Vision.基于计算机视觉的煤矸分选机器人系统
Sensors (Basel). 2021 Feb 14;21(4):1349. doi: 10.3390/s21041349.
8
Research on Recognition of Coal and Gangue Based on Laser Speckle Images.基于激光散斑图像的煤与矸石识别研究
Sensors (Basel). 2023 Nov 11;23(22):9113. doi: 10.3390/s23229113.
9
Image feature extraction and recognition model construction of coal and gangue based on image processing technology.基于图像处理技术的煤与矸石图像特征提取及识别模型构建
Sci Rep. 2022 Dec 5;12(1):20983. doi: 10.1038/s41598-022-25496-5.
10
Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm.基于改进的YOLOv7-Tiny目标检测算法的煤与矸石识别研究
Sensors (Basel). 2024 Jan 11;24(2):456. doi: 10.3390/s24020456.

本文引用的文献

1
Precise detection of coal and gangue based on natural γ-ray.基于天然γ射线的煤与矸石精确检测
Sci Rep. 2024 Jan 13;14(1):1276. doi: 10.1038/s41598-024-51424-w.
2
Image feature extraction and recognition model construction of coal and gangue based on image processing technology.基于图像处理技术的煤与矸石图像特征提取及识别模型构建
Sci Rep. 2022 Dec 5;12(1):20983. doi: 10.1038/s41598-022-25496-5.
3
Image variability and face matching.图像变化和面部匹配。
Perception. 2022 Nov;51(11):804-819. doi: 10.1177/03010066221119088. Epub 2022 Aug 22.
4
Application of unmanned aerial vehicle (UAV) thermal infrared remote sensing to identify coal fires in the Huojitu coal mine in Shenmu city, China.无人机热红外遥感技术在中国神木市火鸡图煤矿煤火识别中的应用。
Sci Rep. 2020 Aug 17;10(1):13895. doi: 10.1038/s41598-020-70964-5.