Study of UGS Target Recognition Method based on Recurrent Neural Network and Peak Value Region Extraction

Author(s):  
Wei Zhao ◽  
Xiong Li ◽  
Nan Wang ◽  
Tonghua Xu
2020 ◽  
Vol 14 (8) ◽  
pp. 1689-1697
Author(s):  
Haiyan Du ◽  
Chunxue Wu ◽  
Yan Wu ◽  
Ren Han ◽  
Xiao Lin ◽  
...  

Abstract In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.


Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2008
Author(s):  
Lu ◽  
Zhang ◽  
Xu ◽  
Lin ◽  
Huo

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.


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