An annotation assistant for monitoring the electrical grid using aerial images

Abstract

Monitoring the electrical grid is essential to ensure reliable service and prevent accidents. This supervision is performed by aerial vehicles for image collection; later, these collected images are processed and analyzed by expert annotators. Due to the high costs of manually handling such as large datasets, we present a novel hybrid methodology that leverages deep learning to reduce and optimize annotation workload. The approach uses annotator-provided labels to train a neural network that makes annotation suggestions and gradually reduces the manual workload. Our work is closely related to active learning, but with a key difference: all data must be labeled and verified to guarantee correctness. Therefore, our methodology focuses on reducing the annotation time rather maximizing model performance. Our hybrid method assists annotators by suggesting annotations on high-confidence images that only need verification instead of being created from scratch. Using the proposed approach, annotators can complete their task at least 2.67x faster than with the previous fully manual labeling procedure.