基于CotNet改进YOLOv5的接地线目标检测
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

农业部水产养殖数字建设试点项目(2017-A2131-130209-K0104-004);青岛市创新创业领军人才(15-07-03-0030);国家自然科学基金(61806107)


CotNet-based Improved YOLOv5 for Grounding Line Target Detection
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    在化工厂区的油罐车装卸区, 防止油罐车静电的产生和危害是避免加油车燃爆的重要手段, 通过静电接地线可以将油罐车感应的静电导走, 避免与外界物质产生跳火. 如何确保接地线在装卸车流程中正确安装不会被意外拆卸或提前拆卸是厂区急需解决的问题. 为确保在防爆区内用防爆摄像头的情况下能够对实时画面进行实时检测, 针对接地线连接角度不一, 拉伸后变细等特点提出将深度学习YOLOv5目标检测算法通过引入自注意力机制CotNet的方法. 在自制的接地线数据集上进行算法的检测速度和检测精度对比, 实验结果表明, 改进后的YOLOv5算法在速度稍有降低的情况下提高了5%的检测精度, 可以满足现场接地线检测需求.

    Abstract:

    In the tanker loading and unloading area in a chemical plant area, preventing the generation and harm of static electricity in the tanker is an important means to avoid the combustion and explosion of the tanker. The static electricity induced by the tanker can be conducted away by the electrostatic grounding line to avoid sparkover with external substances. How to ensure that the grounding line is correctly installed during the loading and unloading process and will not be accidentally disassembled or disassembled in advance is an urgent problem to be solved in a plant area. To ensure that real-time images can be detected in real time when explosion-proof cameras are used in the explosion-proof area, this study gives due consideration to the characteristics, including different connection angles and thinning under stretching, of grounding lines and proposes a deep learning you only look once version 5 (YOLOv5) target detection algorithm by introducing the self-attention mechanism CotNet. The detection speed and detection accuracy of the proposed algorithm are compared on a self-made grounding line dataset. The experimental results show that the improved YOLOv5 algorithm, increasing the detection accuracy by 5% at the cost of a slight decrease in speed, can meet the needs of on-site grounding line detection.

    参考文献
    相似文献
    引证文献
引用本文

黄昊,李海涛.基于CotNet改进YOLOv5的接地线目标检测.计算机系统应用,2023,32(5):283-290

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-09-29
  • 最后修改日期:2022-10-27
  • 录用日期:
  • 在线发布日期: 2023-02-17
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号