Abstract
Surface object recognition plays an important role in surface detection system. Comparing with feature-based classifier, deep neural networks have evolved to the state-of-the-art technique for object recognition in complex background. However, excessive memory requirements, expensive computational costs and overmuch energy consumption make it difficult to deploy neural networks on embedded platform such as the environment perception module of the Unmanned surface vessel (USV). In this paper, we propose a dynamic-selecting criterion approach to prune a trained Yolo-v2 model to deal with these drawbacks caused by redundant parameters in network and we can reduce inference costs for Yolo-v2 by up to 65% on it while regaining close to the original performance by retraining the network. Moreover, we introduce a surface object dataset for surface detection system.
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This work was supported by the Shanghai Aerospace Science and Technology Innovation Program under Grant sast2016063.
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Lin, W. et al. (2019). Efficient ConvNet for Surface Object Recognition. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11741. Springer, Cham. https://doi.org/10.1007/978-3-030-27532-7_28
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