基于两阶段深度网络的输电线路异常目标检测方法
作者:
作者单位:

1.青岛科技大学;2.澳门城市大学

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中图分类号:

TP391

基金项目:

国家自然科学基金项目(61702295),江西省自然科学基金(20202BABL212001),山东省自然科学基金(ZR2020QF003)


Transmission Line Abnormal Object Detection Method Based on Deep Network of Two-stage
Author:
Affiliation:

1.Qingdao University of Science and Technology;2.City University of Macau

Fund Project:

National Natural Science Foundation of China(Grant No. 61702295), the JiangXi Province Natural Science Foundation (Grant No. 20202BABL212001),the Shandong Province Natural Science Foundation (Grant No. ZR2020QF003),

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    摘要:

    输电线路的异常目标检测对提高输电系统的安全性、可靠性、稳定性起到十分重要的作用,而已有目标检测并未针对线路异常目标的尺度变化大、小目标多、光线暗、部分遮挡等问题进行有效设计,导致识别速度慢、易受环境干扰、误报漏报频发等。针对上述问题,本文采用两阶段深度网络,利用FPN提取多尺度特征,使主干网更好的适应目标多尺度变化,并通过全局网络进行特征增强,获得更清晰、更具有代表性的多尺度目标特征。在RPN中提出特征指导的候选框生成网络,能够生成稀疏且形状任意的锚,产生更紧密的掩模包围框。在检测阶段,采用多任务损失函数提升网络的预测精度和泛化能力,提高异常目标的检测性能。在MS COCO数据集上进行消融实验和性能对比,证明了提出方法的有效性和先进性,在输电线路数据集上异常目标检测精度达到77%,优于主流深度学习的目标检测方法。

    Abstract:

    Abnormal object detection of transmission lines plays a very important role in improving the safety, reliability and stability for transmission system, but existing object detection has not been effectively designed for large scale changes, many small objects, dark light, partial occlusion of abnormal objects on the line, resulting in recognition speed slow, susceptibility to environmental interference, and frequent false alarms. In response to the above problems, this paper adopts a two-stage deep network. FPN is used to extract multi-scale features so that backbone network can better adapt to multi-scale changes of object, and feature enhancement is performed through global network to obtain clearer and representative multi-scale object features. A feature-guided region proposal generation network is proposed in RPN, which can generate sparse and arbitrary-shaped anchors, generate tighter mask bounding boxes and improve detection performance of abnormal objects. In detection stage, a multi-task loss function is used to improve prediction accuracy and generalization ability of network, and to improve detection performance of abnormal objects. Ablation experiment and performance comparison on MS COCO dataset prove the effectiveness and advancement of proposed method. The detection accuracy of abnormal objects on transmission line dataset reaches 77%, which is better than mainstream deep learning object detection methods.

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历史
  • 收稿日期:2020-12-31
  • 最后修改日期:2021-04-25
  • 录用日期:2021-05-12
  • 在线发布日期: 2021-07-01
  • 出版日期: