In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.
Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model.
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