An incremental intelligent object recognition system based on deep learning
Yan Long, Yongxiong Wang, Tianzhong Song, Zhong Yin · 2017
The accuracy of object recognition has been greatly improved due to the rapid development of deep learning, but the deep learning generally requires a lot of training data and the training process is very slow and complex. We propose an incremental object recognition system based on deep learning techniques and speech recognition technology with high learning speed and wide applicability. The system can learn from scratch through the way of human-computer interaction. Through the interaction of user, the system continues to improve its identification ability by updating or adding object's feature templates gradually. The types of objects that it can be identified become more and more, and recognition rate is also getting high increasingly. The GoogLeNet inception v4 network is used to extract the object features. Then the object is classified based on the extracted features by measuring the similarity between the object and its template. Experiments show that our system can identify the object accurately after the system learns about ten samples of this object. The self-learning system has a wide range of applicability and flexibility because of the incremental frame based on deep learning.