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摘要
针对目前基于深度学习的输电线路目标检测存在的小目标特征提取能力较差、易出现误检漏检、检测精度较低、检测速度较慢等问题，提出了一种基于改进深度学习神经网络模型YOLOv7的目标检测方法。首先使用MobileNetV2网络作为YOLOv7的特征提取部分，实现模型的轻量化处理；其次引入注意力（coordinate attention，CA）机制和空洞金字塔池化（atrous spatial pyramid pooling，ASPP）模块来提高模型的精度和感知能力；最后利用建立的输电线路障碍物数据集来训练改进的YOLOv7网络模型，并与原有YOLOv7网络模型进行对比。结果表明，算法在准确率、召回率上显著提升，可满足复杂场景下的输电线路故障检测，更利于模型的嵌入式系统硬件实现。
关键词
输电线路
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目标检测
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神经网络
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轻量化模型
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注意力机制
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深度学习
Key words
transmission line
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target detection
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neural network
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lightweight model
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attention mechanism
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deep learning
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刘艳梅,陈鑫顺,陈震,孙改生.
基于深度学习的输电线路目标检测[J].
沈阳航空航天大学学报, 2024, 41(2): 68-75 DOI:10.3969/j.issn.2095-1248.2024.02.008
基金资助
教育部春晖计划(HZKY20220431)
国网辽宁省电力有限公司科技项目(SGLNYJ00 QXJS2200005)
京公网安备11010202008535号
