Abstract
Battlefield environments are harsher than normal environments, and the images captured by imaging equipment are more prone to degradation. Degraded images seriously affect the analysis of military intelligence and the deployment of intelligent weapons. To address this issue, we propose an image recovery algorithm, which recovers degraded battlefield images based on a physical imaging model and uses a light-weight network to estimate the parameters of physical model. In addition, we propose a strategy to joint training of the image recovery module and the object detection module. Specifically, we integrate the recovery module to the front of YOLO detector to jointly optimize the two modules with detection loss. The image recovery module is light-weight without significant adverse impact on the real-time running of object detection, which can be easily deployed to intelligent unmanned devices such as battlefield robots. The experimental results show that the proposed algorithm achieves better recovery performance in battlefield environments, and the joint training strategy effectively improve the accuracy of object detection.
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Acknowledgements
This work was supported by the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant 2022196 and Y202051, in part by the National Natural Science Foundation of China under Grant 61821005, in part by the Natural Science Foundation of Liaoning Province under Grant 2021-BS-023.
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Wang, X., Chen, X., Wang, F., Xu, C., Tang, Y. (2023). Image Recovery and Object Detection Integrated Algorithms for Robots in Harsh Battlefield Environments. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14270. Springer, Singapore. https://doi.org/10.1007/978-981-99-6492-5_49
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DOI: https://doi.org/10.1007/978-981-99-6492-5_49
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