[1]王巍,杨耀权,王乾铭,等.嵌入视觉关系掩码的多类别金具检测方法[J].智能系统学报,2023,18(3):440-449.[doi:10.11992/tis.202202021]
WANG Wei,YANG Yaoquan,WANG Qianming,et al.A multi-category fitting detection method with embedded visual relation masks[J].CAAI Transactions on Intelligent Systems,2023,18(3):440-449.[doi:10.11992/tis.202202021]
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WANG Wei,YANG Yaoquan,WANG Qianming,et al.A multi-category fitting detection method with embedded visual relation masks[J].CAAI Transactions on Intelligent Systems,2023,18(3):440-449.[doi:10.11992/tis.202202021]
嵌入视觉关系掩码的多类别金具检测方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第3期
页码:
440-449
栏目:
学术论文—机器学习
出版日期:
2023-07-05
- Title:
- A multi-category fitting detection method with embedded visual relation masks
- Keywords:
- target detection; transmission line; fittings; deep learning; visual relationship; prior knowledge; spatial information; auxiliary information
- 分类号:
- TP183
- 摘要:
- 在对输电线路金具进行检测的过程中，由于受到复杂背景的影响，一些互相遮挡或者特征不明显的金具会隐匿在复杂环境难以精确检测。针对这一问题，提出了基于视觉关系掩码的多类别金具检测模型，通过挖掘和提取输电线路金具之间包含空间信息的视觉关系先验知识，构建视觉关系掩码和视觉关系检测网络，并将先验知识作为辅助信息融入视觉关系模块中，最终实现多类别金具的精确定位与识别。对具有14类金具的数据集进行多种定性和定量实验，结果表明，改进后的模型平均检测精度能提高到76.25%，检测效果也优于其他先进目标检测模型。
- Abstract:
- In the process of detecting power transmission line fittings, due to the influence of complex backgrounds, some fittings with mutual obscuration or inconspicuous features will be hidden in complex environments and it is hard to detect them accurately. In response to this problem, this paper proposes a multi-category hardware detection model based on visual relationship masks. By mining and extracting the prior knowledge of visual relationships between power transmission line fittings that contain spatial information, the visual relationship mask(VRM), and visual relationship detection network(VRDN) are constructed, and the prior knowledge is integrated into the visual relationship module as auxiliary information, realizing the precise positioning and recognition of multi-category fittings. A variety of qualitative and quantitative experiments have been performed on a data set with 14 types of hardware. The results show that the average detection accuracy of the improved model can be increased to 76.25%, and the detection effect is better than other advanced target detection models.
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备注/Memo
收稿日期:2022-02-24。
基金项目:国家自然科学基金项目(U21A20486，61871182)；河北省自然科学基金项目(F2021502008).
作者简介:王巍,硕士研究生,主要研究方向为图像处理和电力视觉。;杨耀权,教授,博士,主要研究方向为计算机视觉、智能测控技术。主持河北省科技计划项目1项,主持横向科研项目12项。发表学术论文80余篇。;翟永杰,教授,博士,主要研究方向为电力视觉。主持国家自然科学基金面上项目1项、河北省自然科学基金项目1项,主持横向科研项目16项,参与国家重点研发计划项目1项,授权发明专利10项,获山东省科技进步一等奖1项。发表学术论文30余篇,编著1部,参编著作3部,参编教材1部。
通讯作者:翟永杰.E-mail:zhaiyongjie@ncepu.edu.cn
更新日期/Last Update:
1900-01-01