An adaptive dual-weighted feature network for insulator detection in transmission lines

被引：0
作者：
Jie Zhang [1 ]
Xiabing Wang [1 ]
Yinhua Li [1 ]
Dailin Li [1 ]
Fengxian Wang [1 ]
Linwei Li [1 ]
Huanlong Zhang [1 ]
Xiaoping Shi [2 ]
机构：
[1] College of Electric and Information Engineering, Zhengzhou University of Light Industry, No.5 Dongfeng Road, Jinshui District, Henan Province, Zhengzhou
[2] Control and Simulation Center, Harbin Institute of Technology, No.2 yikuang street, Nangang District, Heilongjiang Province, Harbin
来源：
Neural Computing and Applications | 2025年 / 37卷 / 10期
基金：
中国国家自然科学基金;
关键词：
ADFNet; Contextual features; Cross-scale residual perception network; Insulator detection; Small objects;
D O I：
10.1007/s00521-024-10957-x
中图分类号：
学科分类号：
摘要：
In the field of electrical power applications, high-voltage insulators necessitate routine inspection to assure the security and stability of the whole electric power system operation. Accurately positioning the insulator is extremely crucial for proceeding to the insulator defect detection. However, during UAV electrical line inspection, the presence of the electric power line magnetic field engenders a reduction in the pixel representation of the insulator within the image data, thereby diminishing the accuracy of insulator detection. In response to the prevailing issues, we present the creation of the adaptive dual-weighted feature network in this paper. Simultaneously, we create an insulator dataset to substantiate the effectiveness of enhanced model in detecting small insulators. Firstly, the integration of context fusion network is employed to capture comprehensive contextual features for each effective feature map. In addition, a cross-scale residual perception network is incorporated into the neck prior to three concatenation modules, facilitating the collection of diverse information across levels. Finally, a Dual-Weighted Feature Fusion module is designed to replace the conventional concatenation pattern within the neck, thus achieving a more precise representation of object features. Experiments are conducted on the insulator dataset, the RSOD dataset and the NWPU VHR-10 dataset to evaluate the designed model, resulting in mAP values that were 3.92%, 1.55% and 2.39% higher than the YOLOv7, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.
引用
收藏
页码：7067 / 7087
页数：20
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