Abstract
The training of deep learning networks for robot object recognition requires a large database of training images for satisfactory performance. The term “dynamic learning” in this paper refers to the ability of a robot to learn new features under offline conditions by observing its surrounding objects. A training framework for robots to achieve object recognition with satisfactory performance under offline training conditions is proposed. A coarse but fast method of object saliency detection is developed to facilitate raw image collection. Additionally, a training scheme referred to as a Dynamic Artificial Database (DAD) is proposed to tackle the problem of overfitting when training neural networks without validation data.
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Acknowledgement
This work is supported by Defence Innovative Research Programme (DIRP), the Ministry of Defence, Singapore under grant R-263-000-B08-592.
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Chan, J.Y., Ge, S.S., Wang, C., Li, M. (2016). Data Augmentation for Object Recognition of Dynamic Learning Robot. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_41
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DOI: https://doi.org/10.1007/978-3-319-47437-3_41
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