Abstract
Based on Global Positioning System (GPS), Inertial Measurement Unit (IMU) and altitude calculation to obtain the attitude and position information of Unmanned Aerial Vehicle (UAV) for image alignment, this method significantly improves the stitching speed and stitching quality compared with the traditional image matching. However, as UAVs can take thousands of images, the images can be blurred and distorted, making manual screening time consuming and difficult to ensure that the right reference image is selected. In order to be able to select the right reference image for the final stitching quality. This paper proposed a Deep Q-Network (DQN)-based image stitching algorithm for UAVs (IMDQN). The UAV captured images were preprocessed and fed into DQN, and the globally optimal reference image was decided by the action selection model, Root Mean Square Error (RMSE) metrics were also used as an incentive mechanism. The action selection model was used to select the optimal reference image to integrate the input to Position and Orientation System (POS) data for image alignment operation, while the final stitching was accomplished using the weighted average fusion algorithm for picture fusion. The study’s findings demonstrate the algorithm’s clear autonomy and usefulness above the conventional approach, particularly given the enhanced image stitching quality. The study’s findings aid in the creation of later picture stitching methods.
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Ma, J., Liu, W., Chen, T. (2023). DQN-Based Stitching Algorithm for Unmanned Aerial Vehicle Images. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_9
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