[1]赵振兵,郭广学,王艺衡,等.融合边缘感知与统计纹理知识的输电线路金具锈蚀检测[J].智能系统学报,2024,19(5):1228-1237.[doi:10.11992/tis.202306009]
ZHAO Zhenbing,GUO Guangxue,WANG Yiheng,et al.Rust detection in transmission line fittings via fusion of edge perception and statistical texture knowledge[J].CAAI Transactions on Intelligent Systems,2024,19(5):1228-1237.[doi:10.11992/tis.202306009]
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ZHAO Zhenbing,GUO Guangxue,WANG Yiheng,et al.Rust detection in transmission line fittings via fusion of edge perception and statistical texture knowledge[J].CAAI Transactions on Intelligent Systems,2024,19(5):1228-1237.[doi:10.11992/tis.202306009]
融合边缘感知与统计纹理知识的输电线路金具锈蚀检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第5期
页码:
1228-1237
栏目:
学术论文—智能系统
出版日期:
2024-09-05
- Title:
- Rust detection in transmission line fittings via fusion of edge perception and statistical texture knowledge
- Keywords:
- target detection; semantic segmentation; transmission lines; rust detection; fitting; attention mechanism; statistical textures; edge perception; knowledge fusion.
- 分类号:
- TP183
- 文献标志码:
- 2024-08-29
- 摘要:
- 针对输电线路金具目标小、背景环境复杂和锈蚀区域不规则等问题，提出了一种融合边缘感知与统计纹理知识的输电线路金具锈蚀检测算法。首先通过改进YOLOv7模型检测金具，然后利用改进Res-UNet模型对检测的金具进行锈蚀分割，加入SE(squeeze-excitation)注意力提高模型的稳定性，引入统计纹理知识模块(statistical texture knowledge module, STM)和边缘感知模块(edge-aware module, EAM)，提出一种知识融合模块对边缘感知和统计纹理知识进行融合，提高对锈蚀分割精度。实验结果表明，检测和分割模型mAP分别提高了2.8百分点和7.7百分点。
- Abstract:
- To address issues such as small target sizes, complex background environments, and irregular rust areas, a new rust detection algorithm combining edge sensing and statistical texture knowledge is proposed. First, the YOLOv7 model is improved for detecting fittings. Furthermore, the enhanced Res-UNet model is used for corrosion segmentation on the detected fittings. Additionally, squeeze excitation is incorporated to improve the stability of the model. The statistical texture knowledge module and edge-aware module are introduced, and a knowledge fusion module is proposed to integrate edge perception with statistical texture knowledge to enhance the precision of rust segmentation. Experimental results show that the detection and segmentation models increased by 2.8 percentage points and 7.7 percentage points, respectively.
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备注/Memo
收稿日期:2023-6-6。
基金项目:国家自然科学基金项目(U21A20486, 62373151, 62371188, 62303184)；河北省自然科学基金项目(F2021502008, F2021502013)；中央高校基本科研业务费专项资金项目(2023JC006).
作者简介:赵振兵，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项目等纵向课题10项，获省科技进步奖一等奖2项，以第一完成人获得国家专利授权16项，以第一作者出版专著2部，发表学术论文50余篇。E-mail：zhaozhenbing@ncepu.edu.cn;郭广学，硕士研究生，主要研究方向为电力视觉、知识表示与推理。E-mail：ggx3634@163.com;赵文清，教授，博士生导师，博士，中国计算机学会高级会员，主要研究方向为人工智能与能源、电力视觉。主持国家自然科学基金、河北省自然科学基金等项目10余项，获得河北省科技进步奖二等奖。发表学术论文80余篇。E-mail：zhaowenqing@ncepu.edu.cn。
通讯作者:赵振兵. E-mail：zhaozhenbing@ncepu.edu.cn
更新日期/Last Update:
2024-09-05