End-to-End Change Detection for High Resolution Drone Images with GAN Architecture
This addresses infrastructure monitoring problems for drone operators, but it appears incremental as it applies an existing GAN framework to a new domain.
The paper tackles change detection in high-resolution drone images for infrastructure inspection, specifically solar panel installation, using a GAN-based approach and demonstrates that it outperforms other state-of-the-art methods.
Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection. We demonstrate this concept on solar panel installation. A deep learning, data-driven algorithm for identifying changes based on a change detection deep learning algorithm was proposed. We use the Conditional Adversarial Network approach to present a framework for change detection in images. The proposed network architecture is based on pix2pix GAN framework. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art change detection methods.