Automatic alignment of container corner based on monocular vision

ZHANG Yonglei; HU Tao; SHEN Liqun

doi:10.37188/OPE.20253319.3150

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Automatic alignment of container corner based on monocular vision

Information Sciences | 更新时间：2025-11-03

- Automatic alignment of container corner based on monocular vision
- Optics and Precision Engineering Vol. 33, Issue 19, Pages: 3150-3161(2025)
- 作者机构：
  
  哈尔滨工业大学仪器科学与工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20253319.3150
  CLC： TH247;TP29
- CSTR：32169.14.OPE.20253319.3150
- Received：16 June 2025，
  
  Revised：2025-07-20，
  
  Published：10 October 2025
- 稿件说明：
移动端阅览
张永雷,胡涛,申立群.基于单目视觉的集装箱角件自动对位[J].光学精密工程,2025,33(19):3150-3161.

ZHANG Yonglei,HU Tao,SHEN Liqun.Automatic alignment of container corner based on monocular vision[J].Optics and Precision Engineering,2025,33(19):3150-3161.
张永雷,胡涛,申立群.基于单目视觉的集装箱角件自动对位[J].光学精密工程,2025,33(19):3150-3161. DOI： 10.37188/OPE.20253319.3150. CSTR： 32169.14.OPE.20253319.3150.

ZHANG Yonglei,HU Tao,SHEN Liqun.Automatic alignment of container corner based on monocular vision[J].Optics and Precision Engineering,2025,33(19):3150-3161. DOI： 10.37188/OPE.20253319.3150. CSTR： 32169.14.OPE.20253319.3150.

摘要

为了提高集装箱装卸效率，实现装卸自动化，基于单目视觉，结合深度学习和图像处理算法，提出了一种吊具与集装箱的自动对位技术。以单目相机拍摄的集装箱吊装工况图像为研究对象，分析角件区域特征。针对角件在高分辨率图像中像素占比低的问题，提出“粗定位-精分割”的处理方法。然后，基于分割结果检测关键特征点，构建2D-3D点对，通过L-M算法完成位姿估计。最后，在实验室AUBO-i10机械臂对位平台上开展自动对位实验，验证所提算法的有效性。实验结果表明，在实验室环境和真实场景下，集装箱角件的检测精度mAP值均达到95%以上，角件分割精度mIoU值分别达到98.15%和93.89%，相比原模型SegFormer-B0分别提高1.24%和1.64%，模型计算量下降约23

.2%；在相机距离集装箱角件2 m左右的情况下，对位位置瞄准误差小于1.0 mm，在

，

3个轴上自动对位的对位平移绝对误差小于5.0 mm，旋转绝对误差小于0.5°。结果证明，本文所提算法精度可靠，满足单角件自动对位需求。

Abstract

To enhance the efficiency of container loading and unloading and realize automated operations， this study investigates monocular-vision-based automatic alignment between spreader and container， integrating deep learning and image-processing techniques. Monocular images of container hoisting conditions were analyzed with emphasis on the regional characteristics of corner components. To address the low pixel proportion of corner regions in high-resolution images， a two-stage "coarse positioning-fine segmentation" strategy was proposed. Based on the segmentation results， key feature points were detected， 2D-3D point correspondences were established， and pose estimation was performed using the Levenberg-Marquardt algorithm. Validation was conducted on an AUBO-i10 manipulator alignment platform in laboratory settings. Experimental results demonstrate that the mean average precision （mAP） for detection of container corner components exceeds 95% in both laboratory and real-scene environments. Mean intersection-over-union （mIoU） for corner segmentation reached 98.15% and 93.89%， respectively-improvements of 1.24% and 1.64% over the baseline SegFormer-B0-while model computational cost was reduced by approximately 23.2%. At a camera-to-corner distance of about 2 m， the aiming error of the alignment position was below 1.0 mm. Absolute translation errors on the

，

， and

axes were all below 5.0 mm， and absolute rotation error was below 0.5°. These results indicate that the proposed method achieves reliable accuracy and satisfies the requirements for automatic alignment of single-angle components.

关键词

Keywords

references

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