华东师范大学学报(自然科学版) ›› 2025, Vol. 2025 ›› Issue (4): 28-37.doi: 10.3969/j.issn.1000-5641.2025.04.003

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基于高完备性自建数据集的集装箱锁销识别

徐星星, 黄昶*()   

  1. 华东师范大学 通信与电子工程学院, 上海 200241
  • 收稿日期:2024-01-22 出版日期:2025-07-25 发布日期:2025-07-19
  • 通讯作者: 黄昶 E-mail:chuang@ee.ecnu.edu.cn

Container lock pins recognition based on a self-built dataset with high completeness

Xingxing XU, Chang HUANG*()   

  1. School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
  • Received:2024-01-22 Online:2025-07-25 Published:2025-07-19
  • Contact: Chang HUANG E-mail:chuang@ee.ecnu.edu.cn

摘要:

集装箱锁销的识别精度是影响码头自动化拆卸效率的关键因素之一. 由于集装箱锁销形状和结构各异, 现有的传统手工制作描述符进行锁销识别等方法存在速度慢、准确率低的问题. 提出了一种基于高完备性自建数据集的集装箱锁销3D点云识别方法, 通过搭建3D点云数据半自动采集系统采集大量集装箱锁销的3D点云数据, 构建高完备性集装箱锁销3D点云数据集. 基于自建数据集, 使用点云分类深度学习网络算法训练模型, 验证构建数据集方法的合理性以及数据集的有效性. 结果表明, 基于高完备性自建数据集的锁销识别方法精度高, 其完整单视角3D点云识别准确率可以达到100%, 且不易受外界环境因素干扰, 在缺失部分点云情况下也有良好的表现.

关键词: 集装箱锁销, 自建数据集, 3D点云, 锁销识别

Abstract:

The recognition accuracy of container lock pins is a main factor affecting the efficiency of automatic dismantling of terminals. However, the shape and structure of container lock pins are different, and traditional methods, such as hand-made descriptors for lock pin recognition, are slow and inaccurate. This study proposes a 3D point cloud recognition method for container locking pins based on self-built datasets with high completeness; additionally, a 3D point cloud dataset of container locking pins with high completeness is constructed by realizing a semi-automatic collection system for 3D point cloud data collection of many container locking pins. Based on the self-built dataset, a point cloud classification deep learning network algorithm is used to train the model to verify the rationality of the method of constructing the dataset and the effectiveness of the dataset. The results indicate that the lock-pin recognition method based on the self-built dataset with high completeness has high accuracy. Moreover, the recognition accuracy of a complete single-view 3D point cloud can reach 100%, it is not easily affected by external environmental factors, and it has good performance in the case of missing parts of the point cloud. The research results can provide theoretical and technical support for the automatic identification and disassembly of container lock pins in terminals.

Key words: container lock pins, self-managed dataset, 3D point cloud, lock identification

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