Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132013, China.
Guangdong Electric Power Corporation, Zhuhai Power Supply Bureau, Zhuhai 519000, China.
Comput Intell Neurosci. 2022 Sep 3;2022:8955292. doi: 10.1155/2022/8955292. eCollection 2022.
Defective insulator detection is an essential part of transmission line inspections based on unmanned aerial vehicles. It can timely discover insulator defects and repair them to avoid a power transmission accident. The detection speed of defective insulators based on artificial intelligence directly affects inspection efficiency. To improve the detection speed of defective insulators based on YOLOv5s, an improved detection method with faster detection speed and acceptable precision is proposed. First, a new ResNet unit with three branches is designed based on depthwise separable convolution with kernel three and average pooling. To reduce parameters, the new ResNet unit is used to replace the original ResNet unit used in the CSP1_X module in YOLOv5s. Besides, we also introduce channel shuffle in the CSP1_X module to facilitate the flow of feature information from different channels. Second, a new residual CBL module is designed based on depthwise separable and standard convolution. The new residual CBL module is used to replace the two CBL modules used in the CSP2_X module in YOLOv5s to reduce parameters and extract more useful features. Third, we design a separate, coordinated attention module by introducing location information into channel attention. The new attention module is added to the end of the CSP2_X module to improve the ability to extract insulator location information. Besides, we also use convolution to replace the focus model to reduce computation. Compared with defective insulator detection methods, the proposed method has smaller parameters, floating-point operations per second, and higher frames per second. Although it has lower mean precision, it has a faster detection speed. Besides, the increase in detection speed is greater than the decrease in mean precision.
缺陷绝缘子检测是基于无人机的输电线路巡检的重要组成部分。它可以及时发现绝缘子缺陷并进行修复，避免输电事故的发生。基于人工智能的缺陷绝缘子检测速度直接影响检测效率。为了提高基于 YOLOv5s 的缺陷绝缘子检测速度，提出了一种具有更快检测速度和可接受精度的改进检测方法。首先，基于带有平均池化的 3 核深度可分离卷积设计了一个具有三个分支的新 ResNet 单元。为了减少参数，新的 ResNet 单元用于替换 YOLOv5s 中 CSP1_X 模块中原有的 ResNet 单元。此外，我们还在 CSP1_X 模块中引入通道混洗，以促进不同通道的特征信息的流动。其次，基于深度可分离和标准卷积设计了一个新的残差 CBL 模块。新的残差 CBL 模块用于替换 YOLOv5s 中 CSP2_X 模块中的两个 CBL 模块，以减少参数并提取更多有用的特征。第三，通过引入位置信息到通道注意力中，设计了一个独立的、协调的注意力模块。新的注意力模块添加到 CSP2_X 模块的末尾，以提高提取绝缘子位置信息的能力。此外，我们还使用卷积替换焦点模型以减少计算量。与缺陷绝缘子检测方法相比，所提出的方法具有更小的参数、每秒浮点运算数和更高的每秒帧数。尽管它的平均精度较低，但检测速度更快。此外，检测速度的提高大于平均精度的降低。
