Towards accurate image stitching for drone-based wind turbine blade inspection

Accurate image stitching is crucial to wind turbine blade visualization and defect analysis. It is inevitable that drone-captured images for blade inspection are high resolution and heavily overlapped. This also necessitates the stitching-based deduplication process on detected defects. However, the stitching task suffers from texture poor blade surfaces, unstable drone pose (especially off-shore), and the lack of public blade datasets that cater to real-world challenges. In this paper, we present a simple yet efficient algorithm for robust and accurate blade image stitching. To promote further research, we also introduce a new dataset, Blade30, which contains 1,302 real drone-captured images covering 30 full blades captured under various conditions (both on-and off-shore), accompanied by a rich set of annotations such as defects and contaminations, etc. The proposed stitching algorithm generates the initial blade panorama based on blade edges and drone-blade distances at the coarse grained level, followed by fine-grained adjustments optimized by regression-based texture and shape losses. Our method also fully utilizes the properties of blade images and prior information of the drone. Experiments report promising accuracy in blade stitching and defect deduplication tasks in the vision-based wind turbine blade inspection scenario, surpassing the performance of existing methods.