Automatically and accurately detecting the self-blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open-loop cognitive models to detect the self-blast state of glass insulators, this study explores a mixed data augmentation-based intelligent recognition method to detect the self-blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high-quality generative self-blast samples of the glass insulator, and the non-generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi-scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi-deformable convolutional network. Then, the extracted feature maps are transmitted to a two-dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self-blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self-blast state of the glass insulator. Then, based on transfer learning and the established self-optimising feedback mechanism for feature pyramid network, the self-optimising adjustment and reconstruction of the feature map space with strong semantics and multi-scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi-channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.

中文翻译：

---

基于混合数据增强的玻璃绝缘子自爆状态智能识别方法研究

自动、准确地检测玻璃绝缘子的自爆状态对于输电线路的运行和维护具有重要意义。针对现有开环认知模型检测玻璃绝缘子自爆状态的不足，本研究探索了一种基于混合数据增强的智能识别方法来检测玻璃绝缘子自爆状态。人类的认知模式。首先，利用生成对抗网络获得高质量的玻璃绝缘子生成自爆样本，并利用非生成数据增强技术获得丰富的样本特征。其次，考虑到航拍图像尺度变化大、拍摄角度多变、背景复杂等特点，使用具有自适应层次的特征金字塔网络和多变形卷积网络提取具有强语义和自适应多尺度融合的特征图。然后，将提取的特征图传输到二维随机配置网络，该网络可以自适应地生成隐藏节点和基函数，从而制定具有通用逼近能力的自爆炸状态分类标准。再次，基于广义误差与熵理论，定义识别结果语义误差熵评价指标，实时评价玻璃绝缘子自爆状态不确定识别结果的可信度。然后，基于迁移学习和建立的特征金字塔网络自优化反馈机制，实现了强语义、多尺度融合的特征图空间及其分类标准的自优化调整和重构。最后，应用堆叠方法将自适应层次特征金字塔网络和多通道可变形卷积网络的识别结果进行融合，提高识别模型的鲁棒性。与其他机器学习和深度学习方法的实验比较结果验证了该方法的可行性和有效性。采用堆叠方法将自适应层次结构特征金字塔网络和多通道可变形卷积网络的识别结果融合在一起，提高识别模型的鲁棒性。与其他机器学习和深度学习方法的实验比较结果验证了该方法的可行性和有效性。采用堆叠方法将自适应层次结构特征金字塔网络和多通道可变形卷积网络的识别结果融合在一起，提高识别模型的鲁棒性。与其他机器学习和深度学习方法的实验比较结果验证了该方法的可行性和有效性。