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文章摘要
基于改进YOLOv3的输电线路部件实时检测
Real-time detection of transmission line components based on improved YOLOv3
Received:April 14, 2020
Revised:April 14, 2020
DOI：
10.19753/j.issn1001-1390.2023.07.021
中文关键词
:
深度学习
目标检测
输电线路
YOLOv3
轻量化
移动端
英文关键词
:
基金项目
:
Author Name
Affiliation
E-mail
Lu Zhibo
Software College
,
Taiyuan University of Technology
522055239@qq.com
Xu Chengyu
State Grid Shanxi Electric Power Company Internet Department
xcy73@sina.com
Yang Gang
State Grid Shanxi Electric Power Company Electric Power Research Institute
yanggang9800@163.com
Jude Michael Akotonou
Software College
,
Taiyuan University of Technology
judemickael@yahoo.fr
Zhang Xinghzong
*
Taiyuan University of Technology
1659898176@qq.com
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中文摘要
:
针对基于深度学习的目标检测技术应用于工业领域无法在移动端嵌入式设备上实现高效且准确的检测这一问题，提出一种基于YOLOv3改进的输电线路部件实时检测算法轻量级特征融合检测模型LFF-DM(Lightweight Feature Fusion Detection Model)。一方面通过改进式的k-means算法得到聚类结果；另一方面结合深度可分离卷积和倒残差块设计出轻量化的网络结构。通过在自建的包含绝缘子、悬垂线夹、防震锤、鸟巢与导地线的专业巡检数据集上进行实验，结果表明在NVIDIA Jetson AGX Xavier设备上可以实现25FPS的检测速度及90.48%mAP的检测精度，适用于输电线路移动端实时精确巡检。
英文摘要
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