Yan Shuxia, Li Junhuan, Wang Jiachen, Liu Gaohua, Ai Anhai, Liu Rui
School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China.
College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China.
Entropy (Basel). 2023 Sep 14;25(9):1333. doi: 10.3390/e25091333.
With the development of the smart grid, the traditional defect detection methods in transmission lines are gradually shifted to the combination of robots or drones and deep learning technology to realize the automatic detection of defects, avoiding the risks and computational costs of manual detection. Lightweight embedded devices such as drones and robots belong to small devices with limited computational resources, while deep learning mostly relies on deep neural networks with huge computational resources. And semantic features of deep networks are richer, which are also critical for accurately classifying morphologically similar defects for detection, helping to identify differences and classify transmission line components. Therefore, we propose a method to obtain advanced semantic features even in shallow networks. Combined with transfer learning, we change the image features (e.g., position and edge connectivity) under self-supervised learning during pre-training. This allows the pre-trained model to learn potential semantic feature representations rather than relying on low-level features. The pre-trained model then directs a shallow network to extract rich semantic features for downstream tasks. In addition, we introduce a category semantic fusion module (CSFM) to enhance feature fusion by utilizing channel attention to capture global and local information lost during compression and extraction. This module helps to obtain more category semantic information. Our experiments on a self-created transmission line defect dataset show the superiority of modifying low-level image information during pre-training when adjusting the number of network layers and embedding of the CSFM. The strategy demonstrates generalization on the publicly available PASCAL VOC dataset. Finally, compared with state-of-the-art methods on the synthetic fog insulator dataset (SFID), the strategy achieves comparable performance with much smaller network depths.
随着智能电网的发展，输电线路中传统的缺陷检测方法正逐渐转向机器人或无人机与深度学习技术相结合，以实现缺陷的自动检测，避免人工检测的风险和计算成本。无人机和机器人等轻量级嵌入式设备属于计算资源有限的小型设备，而深度学习大多依赖于具有巨大计算资源的深度神经网络。并且深度网络的语义特征更丰富，这对于准确分类形态相似的缺陷以进行检测也至关重要，有助于识别差异并对输电线路组件进行分类。因此，我们提出一种即使在浅层网络中也能获得高级语义特征的方法。结合迁移学习，我们在预训练期间改变自监督学习下的图像特征（例如位置和边缘连通性）。这使得预训练模型能够学习潜在的语义特征表示，而不是依赖于低级特征。然后，预训练模型指导浅层网络为下游任务提取丰富的语义特征。此外，我们引入了一个类别语义融合模块（CSFM），通过利用通道注意力来增强特征融合，以捕获在压缩和提取过程中丢失的全局和局部信息。该模块有助于获得更多的类别语义信息。我们在自行创建的输电线路缺陷数据集上的实验表明，在调整网络层数和嵌入CSFM时，预训练期间修改低级图像信息具有优越性。该策略在公开可用的PASCAL VOC数据集上具有泛化性。最后，与合成雾绝缘子数据集（SFID）上的最新方法相比，该策略在网络深度小得多的情况下实现了可比的性能。
