Vision-based aerial image mosaicking algorithm with object detection

Aerial image sequence mosaicking is one of the chal-lenging research fields in computer vision.To obtain large-scale orthophoto maps with object detection information,we propose a vision-based image mosaicking algorithm without any extra location data.According to object detection results,we define a complexity factor to describe the importance of each input ima-ge and dynamically optimize the feature extraction process.The feature points extraction and matching processes are mainly guided by the speeded-up robust features(SURF)and the grid motion statistic(GMS)algorithm respectively.A robust refer-ence frame selection method is proposed to eliminate the trans-formation distortion by searching for the center area based on overlaps.Besides,the sparse Levenberg-Marquardt(LM)al-gorithm and the heavy occluded frames removal method are ap-plied to reduce accumulated errors and further improve the mo-saicking performance.The proposed algorithm is performed by using multithreading and graphics processing unit(GPU)accel-eration on several aerial image datasets.Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.