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首页> 外文会议>International Conference on Communications, Information System and Computer Engineering >Automatic Detection Method for Small Size Transmission Lines Defect Based on Improved YOLOv3
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Automatic Detection Method for Small Size Transmission Lines Defect Based on Improved YOLOv3

机译：基于改进的YOLOv3的小型输电线路缺陷自动检测方法

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摘要

Defect detection methods based on machine learning extremely accelerate the transmission lines routine inspection process. In this paper, we propose an automatic defect detection method based on improved YOLOv3. Random feature pyramid (RFP) structure is introduced for the highly discriminative feature map construction. Focal loss function, which focus on differentiating between easy and hard examples, is employed to deal with the class imbalance problem. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.
机译：基于机器学习的缺陷检测方法极大地加快了输电线路的常规检查过程。在本文中，我们提出了一种基于改进的YOLOv3的自动缺陷检测方法。引入了随机特征金字塔（RFP）结构以用于高度区分性的特征图构造。焦点损失函数着重于区分简单和难解的例子，用于处理类不平衡问题。实验结果表明，与最新的深度学习对象检测方法相比，该方法具有竞争优势。

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