TransLine: Transfer Learning for Accurate Power Line Anomaly Detection with Insufficient Data

Accurate and automatic power line anomaly detection is critical to smart grid. However, effective solutions are yet available due to the insufficiency of anomaly data. In this paper, we first collect a dataset from various sources consisting both normal and abnormal power line images. With this dataset, anomaly detection becomes feasible though with limited accuracy due to the limited size of the dataset. As such, we propose TransLine, an approach based on transfer learning to apply the existing knowledge extracted from large-scale datasets to complement the data insufficiency of power line anomaly detection. TransLine customizes and optimizes the knowledge to automate the power line anomaly detection with high accuracy. The experiment results show that TransLine can achieve superb accuracy of 96.1% on average and up to 98.1% given only a hundred abnormal images for model training. TransLine can be a key enabler of smart grid for great stability and efficiency and can inspire the other industrial applications facing data insufficiency issues.