Efficient ConvNet for Surface Object Recognition
| Autor: | Jie Ma, Quan Chen, Yongzhi Wang, Bingli Wu, Xianzhi Qi, Dezhao Yang, Wei Lin, Xue Ke |
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| Rok vydání: | 2019 |
| Předmět: |
Unmanned surface vehicle
Artificial neural network Computer science business.industry Deep learning Cognitive neuroscience of visual object recognition Inference Pattern recognition 02 engineering and technology Energy consumption 010501 environmental sciences 01 natural sciences 0202 electrical engineering electronic engineering information engineering Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) 0105 earth and related environmental sciences |
| Zdroj: | Intelligent Robotics and Applications ISBN: 9783030275310 ICIRA (2) |
| DOI: | 10.1007/978-3-030-27532-7_28 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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