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基于SE-YOLOv5s的绝缘子检测
Authors
余兆钗
李佐勇
+3 more
田庆
胡蓉
蔡远征
Publication date
1 September 2021
Publisher
POSTS&TELECOM PRESS Co., LTD
Abstract
在电力系统需要巡检的大环境下,人工巡检的传统方式存在很大不便和安全隐患,而采用无人机的目标检测方法在绝缘子检测识别方向有很大的应用前景。针对绝缘子图像检测中存在的场景复杂、视角多变、设备计算能力受限等问题,提出了一种改进的轻量级SE-YOLOv5s卷积神经网络来实现对绝缘子的快速目标检测,该方法首先在YOLOv5s网络中融合SE注意力模块,以强化网络对绝缘子目标的辨识能力,随后采用K-means聚类方法构建绝缘子的先验框,以提升定位精度,最后构造置信度与定位任务联合的损失函数,并引入 Mosaic 数据增强策略训练网络,有效解决训练数据不足的问题。经过实验验证发现,与主流目标检测方法相比,提出的SE-YOLOv5s方法在绝缘子检测准确率、召回率、检测速度及平均精度均值等性能指标上均取得了较好的结果。实验结果表明,该网络对于绝缘子检测有很好的效果,具有更好的鲁棒性,对电力系统的巡检方式具有参考价值
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Last time updated on 26/01/2023