Defect Detection for Electrical Facilities based on Multispectral Imagery

Regular inspection of electrical facilities plays a significant role in preventing deterioration caused by aging equipment, but the dangers associated with these inspections make it difficult for examiners to perform them without first suspending the power. Currently, these inspections cause shutdowns and result in loss of productivity. For this reason, researchers are actively exploring new ways to detect faults in electrical facilities using deep learning algorithms and various modalities such as single-infrared, single-RGB, and multispectral imagery. Existing methods either use a single modality or fuse two modalities with image registration that decreases technology accessibility. This paper will explore the use of a multispectral anomaly pre-detector based on YOLOv5s, a simple yet effective single-stage detector. The anomaly region detector is a real-world application of this technology that detects parts in the potentially anomalous device and finds the anomalous regions within those parts. In order to verify the two detectors, far- and near-distance datasets were obtained and evaluated using the proposed models. The results indicate that the proposed pipeline enables pixel-level electrical fault detection.