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Fast-moving coin recognition using deep learning
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DOI：10.1007/s11042-021-10857-5.png)
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Deep learning has made essential contributions to the development of visual object detection and recognition. Identifying fast-moving objects from the viewpoint of computer vision remains as a challenging problem. The best solution in deep learning that can well represent the characteristics of object motion is related to recurrent neural network (RNN), the best model for fast-moving object recognition is Long Short-Term Memory (LSTM) in RNN. Therefore, the combination of LSTM and CNN fully utilizes spatial and temporal features of moving objects. In this paper, our goal is to identify fast-moving coins from digital videos by using deep learning methods, especially based on the mixture of LSTM and CNN. By using the proposed method, we gain the attainment with high accuracy of fast-moving coin recognition which is superior to our human visual system.
Keyword:
Coin recognition
Computer vision
Deep learning
Long short-term memory (LSTM)
RNN
CNN
期刊
IF:
3
论文数:
1.9W
被引数:
3.2W
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