Abstract
Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented FAST Rotated BRIEF (ORB)—require heavy computation to extract and describe features in high-resolution UAV images. To overcome this issue, You Only Look Once version 3 (YOLOv3) combined with the traditional feature point matching algorithms is utilized to extract descriptive features from the drone dataset of residential areas for roof detection. Unlike the traditional feature extraction algorithms, YOLOv3 performs the feature extraction solely on the proposed candidate regions instead of the entire image, thus the complexity of the image matching is reduced significantly. Then, all the extracted features are fed into Structural Similarity Index Measure (SSIM) to identify the corresponding roof region pair between consecutive image sequences. In addition, the candidate corresponding roof pair by our architecture serves as the coarse matching region pair and limits the search range of features matching to only the detected roof region. This further improves the feature matching consistency and reduces the chances of wrong feature matching. Analytical results show that the proposed method is 13× faster than the traditional image matching methods with comparable performance.
Highlights
Image registration is a traditional computer vision problem for applications in various domains ranging from military, medical, surveillance, robotics, as well as remote sensing [1].With advances in robotics, cameras can be effortlessly mounted on a UAV to capture the ground images from a top view
After the training of You Only Look Once version 3 (YOLOv3) is completed, the weights generated after the training can be used to detect the candidate overlapping areas of other UAV images
We have evaluated the performance of the YOLOv3-based roof region detection with other cases
Summary
Image registration is a traditional computer vision problem for applications in various domains ranging from military, medical, surveillance, robotics, as well as remote sensing [1]. Cameras can be effortlessly mounted on a UAV to capture the ground images from a top view. A UAV is often operated in a lawn-mower scanning pattern to capture a region of interests (ROI). These captured ROI images are stitched together to provide an overview representation of the entire region. Drones are relatively low-cost and can be operated in remote areas. The process of image stitching is useful in a number of tasks, such as disaster prevention, environment change detection, road surveillance, land monitoring, and land measurement. The task of image matching can be divided into two sub-tasks: feature
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