华东师范大学学报（自然科学版）Issue(4)：28-37,10.DOI:10.3969/j.issn.1000-5641.2025.04.003
基于高完备性自建数据集的集装箱锁销识别
Container lock pins recognition based on a self-built dataset with high completeness
徐星星 1黄昶1
作者信息
- 1. 华东师范大学 通信与电子工程学院,上海 200241
- 折叠
摘要
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.关键词
集装箱锁销/自建数据集/3D点云/锁销识别Key words
container lock pins/self-managed dataset/3D point cloud/lock identification分类
信息技术与安全科学引用本文复制引用
徐星星,黄昶..基于高完备性自建数据集的集装箱锁销识别[J].华东师范大学学报（自然科学版）,2025,(4):28-37,10.
