基于人工图像数据扩充的输电线路绝缘子识别

深度学习方法在计算机视觉领域发展迅速，但依赖于海量训练数据。输电线路绝缘子自动识别任务中，航拍图像数量不足、多样性差等问题影响识别的准确性。提出人工绝缘子图像数据扩充方法，通过3D建模创建人工绝缘子图像，并构建导向反向补偿网络，对创建的人工图像进行补偿优化，用补偿后的人工图像扩充航拍绝缘子图像数据集。在多个典型卷积神经网络上进行绝缘子识别对比实验，结果显示：所提方法使绝缘子识别准确率平均提升2.1%，且网络相对轻量级，验证了所提方法的有效性和优势。

Transmission Line Insulator Recognition Based on Artificial Images Data Expansion

Deep learning method has developed rapidly in the field of computer vision, but relies on a large quantities of training data. In the task of transmission line insulator automatic detection, problems such as insufficient number of aerial insulator images and poor diversity affect the accuracy of insulator recognition. An artificial insulator images data expansion method is proposed. Artificial insulator images are created by modeling software, and a compensation network is constructed. The artificial images are compensated and optimized by compensation network, and the aerial insulator image data set is expanded by the compensated artificial insulator images. The insulator recognition experiments are carried out on several typical convolutional neural networks. The results show that the proposed method improves the accuracy of insulator identification by an average of 2.1%, and the network is relatively lightweight, which verifies the effectiveness and advantages of the proposed method.