中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2021, Vol. 40 ›› Issue (07): 136-144.

• 交通装备 • 上一篇    

面向交通工具金属材料的缺损识别算法

李胜永1,张智华1,王胜男2,王孟2   

  1. (1. 江苏航运职业技术学院 交通工程系,江苏 南通 226000; 2. 南通大学 理学院,江苏 南通 226000)
  • 收稿日期:2019-12-02 修回日期:2020-08-04 出版日期:2021-07-12 发布日期:2021-07-23
  • 作者简介:李胜永(1981—),男,河南濮阳人,副教授,主要从事港口安全与智能控制方面的研究。E-mail:lisy@ntsc.etu.cn
  • 基金资助:
    国家自然科学基金项目(61601249,61601251);南通市科技局科技计划项目(MS12018080);南通航运职业技术学院科技研究项目(HYKY/2017KJA02);江苏省教育厅优秀科技创新团队项目(2017049);江苏省交通运输厅科技研究项目(2018Y26);江苏省高等学校自然科学研究项目(18KJD580002)

Defect Recognition Algorithm for Vehicle Metal Materials

LI Shengyong1, ZHANG Zhihua1, WANG Shengnan2, WANG Meng2   

  1. (1.Department of Traffic Engineering, Jiangsu Shipping College, Nantong 226000, Jiangsu, China; 2. School of Science, Nantong University, Nantong 226000, Jiangsu, China)
  • Received:2019-12-02 Revised:2020-08-04 Online:2021-07-12 Published:2021-07-23

摘要: 金属作为现代交通工具重要设备的主要材料,其缺损情况对交通工具的安全性具有重要意义。为了实现对金属设备的缺损情况进行自动识别,提出一种基于深层卷积神经网络的视觉检测算法,该算法着重于工业缺陷识别。设计了一种沙漏型特征融合模块和金字塔特征细化模块,兼顾准确率和速度,有效提升基于金属图像的缺损部位定位和分类效率,借助计算机平台训练判别模型实现自动检测。算法在公开图像数据集上取得了先进的测试结果,并在移动端设备中实现高效运行。

关键词: 船舶工程, 车辆工程, 金属, 缺损识别, 卷积网络, 分类, 定位

Abstract: As the main material of important equipment in modern transportation, the defect of metal is of great significance to the safety of transportation. In order to realize the automatic identification of the defect of metal equipment, a visual detection algorithm based on deep convolutional neural network was proposed. The proposed algorithm focused on industrial defect identification. An hourglass type feature fusion module and a pyramid feature refinement module were designed, which considered both accuracy and speed, and effectively improved the efficiency of defect location and classification based on metal image. Finally, with the help of computer platform training discriminant model, automatic detection was realized. The proposed algorithm achieves advanced test results on public image datasets and enables efficient operation for mobile devices.

Key words: ship engineering, vehicle engineering, metal, defect identification, convolutional network, classification, positioning

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