Images of Cylinders Transported on a Conveyor Belt - Recording 10

Name: Images of Cylinders Transported on a Conveyor Belt - Recording 10
Creator: Zenodo
Published: 2021-09-14T14:40:39+08:00

Zenodo2021-09-16 更新2026-05-28 收录

数据链接：

https://zenodo.org/record/5145278 数据链接链接失效反馈

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资源简介：

This data set comprises images of cylinders on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer, Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking, Transactions on Industrial Electronics, February 2020. See also the project website. This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at https://doi.org/10.5281/zenodo.5506551 (conveyor belt data sets only). The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on GitHub. Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz. Algorithms for two key challenges can be developed and evaluated on the data sets: Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer, TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials, Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015. Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer, Predictive Tracking with Improved Motion Models for Optical Belt Sorting, at – Automatisierungstechnik, April 2020. Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer, Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information, Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017. Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer, Motion-Based Material Characterization in Sensor-Based Sorting, tm – Technisches Messen, De Gruyter, October 2017. To this date, publications that used these data include Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck, Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking, Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020. CSV-files with already associated particle tracks are available at https://doi.org/10.5281/zenodo.5506551. Acknowledgment The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.

本数据集包含传送带上的圆柱形物料图像，所有图像均通过小型光学带式分选机Tablesort采集。关于Tablesort系统的全面说明，可参阅文献：Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer, 《一种集成预测性实时多目标跟踪的新型传感分选方法的实验评估》，《IEEE工业电子学报》，2020年2月。另可参阅项目官网。本数据集属于光学分选机批量采集数据的一部分。可通过带引号的关键词"Tobias Hornberger"检索获取相关数据集，或仅针对传送带数据集，访问链接https://doi.org/10.5281/zenodo.5506551下载数据列表。图像采集使用Bonito CL-400C相机。外参标定图像为calibration_extrinsics.png，色彩标定图像为calibration_color.png。可参考GitHub上的debayer（拜耳阵列解码）脚本。世界坐标系下，单个像素对应的实际长度约为0.056 mm，图像帧率为192.9 Hz。本数据集可用于开发并评估两类核心挑战的算法： 1. 多目标跟踪与颗粒运动预测：该算法可用于提升光学分选机的分选性能。相关细节可参阅以下两篇文献：Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer, 《TrackSort：面向非合作散料分选的预测性跟踪》，收录于2015年IEEE多传感器融合与智能系统国际会议（MFI 2015）论文集，美国加利福尼亚州圣地亚哥，2015年9月；Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer, 《面向光学带式分选的改进运动模型预测性跟踪》，《自动化技术》，2020年4月。 2. 颗粒分类：可通过多目标跟踪器随时间累积视觉特征实现分类，也可利用颗粒轨迹信息完成分类。相关细节可参阅以下文献：Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer, 《利用运动信息提升传感分选过程中的材料表征精度》，第三届材料光学表征国际会议（OCM 2017）论文集，德国卡尔斯鲁厄，2017年3月；Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer, 《传感分选场景下基于运动的材料表征》，《tm – 技术测量》（De Gruyter出版），2017年10月。截至目前，使用本数据集的已发表文献包括：Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck, 《循环神经网络在多目标跟踪任务中的不确定性估计》，收录于2020年IEEE多传感器融合与智能系统国际会议（MFI 2020）虚拟会议论文集，2020年9月。已完成颗粒轨迹关联的CSV文件可访问https://doi.org/10.5281/zenodo.5506551下载。致谢：由Verfahrens-Technik研究协会（Forschungs-Gesellschaft Verfahrens-Technik e.V.，简称GVT）牵头的IGF项目20354 N，通过由联邦经济事务与能源部依托德国联邦议会决议设立的工业共同体研究与发展（IGF）促进计划，经由AiF获得资助。

提供机构：

Zenodo

创建时间：

2021-09-14