| 宓 超,黄世凤,张钰洁,张志伟,姚 磊.自动化集装箱码头装卸目标三维测量系统设计[J].电子测量与仪器学报,2023,37(2):160-170 |
| 自动化集装箱码头装卸目标三维测量系统设计 |
| Design of 3D measurement system for loading and unloadingtargets in automated container terminals |
| |
| DOI： |
| 中文关键词: 自动化码头 集装箱装卸 集装箱锁孔 三维测量 |
| 英文关键词:automated terminal container handling container keyhole 3D measurement |
| 基金项目:中央军委装备预先研究领域基金(80912020104)、上海市自然科学基金(22ZR1427700)、中国(上海)临港自贸区管理委员会核心技术研发项目(SH-LG-GK-2020-21)资助 |
|
|
|
|
| 摘要点击次数: 533 |
| 全文下载次数: 741 |
| 中文摘要: |
| 针对传统激光雷达在自动化集装箱码头装卸过程中对集装箱三维姿态定位精度低、成本高等问题,提出了一种基于视
觉的集装箱姿态三维测量系统。 首先通过小规模的深度学习网络快速进行集装箱锁孔粗定位,其次通过传统图像处理算法对
集装箱锁孔进行二次定位得到集装箱锁孔的精确位置,最后结合装卸过程中集装箱的物理运动对集装箱姿态进行三维测量。
实验结果表明,与改进前的深度学习网络相比,测量精度更高、测量速度更快;整体算法的测量精度为 93. 71%,约 12. 45 fps,集
装箱姿态测量平均测量误差约为 4. 95%,满足自动化装卸的要求。 |
| 英文摘要: |
| Aiming at the problems of low positioning accuracy and high cost of traditional LiDAR in the process of loading and unloading
of automated container terminals, a vision-based three-dimensional measurement system for container attitude is proposed. Firstly,
through a small - scale deep learning network for rapid coarse positioning container corner, secondly, the traditional image processing
algorithm is used to reposition the container corner pieces to obtain the precise position of the container keyhole, and the threedimensional measurement of the container posture is carried out in combination with the physical movement of the container during the
loading and unloading process. The experimental results show that compared with the deep learning network before improvement, the
measurement accuracy is higher and the measurement speed is faster, the measurement accuracy of the overall algorithm is 93. 71%,
about 12. 45 frames/ s, and the average measurement error of container attitude measurement is about 4. 95%, which meets the
requirements of automatic loading and unloading. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
