Anomaly Detection of Underground Transmission-Line through Multiscale Mask DCNN and Image Strengthening

Abstract

This study proposes an integrated framework to automatically detect anomalies and faults in underground transmission-line connectors (UTLCs) with thermal images because anomaly detection of underground transmission-line connectors (UTLCs) plays a critical role in power line risk management. The proposed framework features three key characteristics. First, the measured thermal images were preprocessed through z-score normalization and image strengthening. Z-score normalization improves the robustness of feature extraction for UTLCs even though noise exists in a thermal image, and image strengthening improves the accuracy of segmentation for UTLCs. Second, a preprocessed thermal image is segmented to detect UTLCs by addressing a multiscale mask deep convolutional neural network (MS mask DCNN). The MS mask DCNN effectively detects UTLCs, enabling anomaly detection only for pixels of UTLCs. Specifically, the multiscale feature extraction module enables the extraction of distinct features of UTLCs and environments, and the skip-layer fusion module concatenates distinct features from the feature extraction module. Furthermore, a half tensor is used to reduce computational resources but maintain the same segmentation accuracy, enhancing the feasibility of the proposed framework in field applications. Third, anomaly detection is performed by addressing the contour method and unsupervised clustering method of DBSCAN. The contour method compensates for the limits of the MS mask DCNN for real-world applications because the neural networks cannot secure perfect accuracy of 100% owing to a lack of sufficient training images and low computational resources. DBSCAN improves the accuracy of diagnosis and ensures robustness to eliminate noise from thermal reflection caused by low-emissivity objects. Field experiments with high-voltage UTLCs demonstrated the effectiveness of the proposed framework. Ablation studies also confirmed that the methods addressed in this study outperform other methods. The proposed framework with a novel automatic non-destructive patrol inspection system would decrease the risks of human casualties during the periodic operation and maintenance of UTLCs, which are currently the most critical concerns.