A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

ABSTRACT

The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.

KEYWORDS:

• UAV remote sensing
• panoramic stitching
• multi-core CPU
• multi process
• edge computing

Acknowledgements

The author thanks the graduate students from the School of Information and Management Sciences of Henan Agricultural University and the National Agricultural Extension Technology Service Center for their continuous support in our research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contributions

Xin Xu and Li Zhang authored this manuscript and conducted on-site UAV image capture. Hongbo Qiao, Xin Xu, Jibo Yue, Jie Liu, and Heming Zhong supervised the UAV capture experiments and provided expertise in UAV mapping. Xin Xu and Li Zhang designed this study. Li Zhang constructed and tested the parallel processing experiments. Ying Wang and Yanhui Lu revise this article. All authors have read and approved the final manuscript.

Data availability statement

The datasets used for the analysis are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was funded by the Key R&D projects during the 14th Five Year Plan period [2022YFD1400302], and the National Natural Science Foundation of China [U2003119].