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<!DOCTYPE html>
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<title>论文专利</title>
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<h2 class="section-title">A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines</h2>
<ul class="project-info">
<li><strong>发表日期:</strong> 2020/05/19</li>
<li><strong>收录:</strong>IEEE</li>
<li><strong>负责人:</strong> 祝金国</li>
<li><strong>Team:</strong> 郭越，岳凡丁，王小华，杨爱军，袁欢</li>
<li><strong>主要贡献:</strong> 将AI与电力深度融合，实现了真正意义上的智能电网！</li>
<li><strong>关键词:</strong> 人工智能，深度学习，电气领域</li>
<!----增加链接一栏---->
<li><strong>论文链接:</strong> https://ieeexplore.ieee.org/abstract/document/9096331</li>
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<p><strong>摘要：</strong>Image online monitoring technology has been widely used in transmission lines inspection, but
the intelligent and efficient foreign object detection still has a gap with the ideal. In this paper, we propose a
deep learning method to detect invading foreign objects for power transmission line inspection.
</p>
<p>Specifically,
we design our network based on the regression strategy with oriented bounding boxes to accurately predict
spatial location and orientation angle of foreign objects, as well as their categories in cluttered backgrounds.</p>
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<!----增加<strong></strong>---->
<p><strong>创新点：</strong>Moreover, an easy yet effective Scale Histogram Matching method is proposed to be applied to the publicly
available dataset, allowing useful patterns to be exploited to detect tiny foreign objects during the pretraining
procedure and boosting detection performance even with limited annotated samples. </p>
<p>Besides, we construct
an image dataset that contains common foreign objects in transmission line scenarios to evaluate proposed
methods, on which experiment results show our full model achieves accuracy with 88.1% mean Average
Precision (mAP). Additionally, the efficient and compact network structure allows our network to run in
real-time, which provides possibilities for practical use.</p>
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