Weakly supervised learning with convolutional neural networks for power line localization

Localization of power lines is important to monitor electricity infrastructures by using unmanned aerial vehicles. Although deep learning is a powerful method to solve computer vision problems, constructing pixel-level ground-truth data for object localization is an exhausting task. This paper proposes a weakly supervised learning algorithm for the localization of power lines by only using image-level class labels. The proposed algorithm classifies sub-regions by using a sliding window and convolutional neural network (CNN). A sub-region is filtered out if it is classified into an image without any power line. If a sub-region is classified into an image with a power line, then its feature maps of intermediate convolutional layers are combined to visualize the location of the power line. Experiments were conducted on actual aerial images to demonstrate the effectiveness of the proposed algorithm.