Information detection method, apparatus, and device

Abstract

An information detection method includes: determining key point information in a target identification from a target image based on a preset deep learning algorithm; obtaining an image of the target identification from the target image according to the key point information; and determining information of a preset field from the image of the target identification according to the image of the target identification and a preset identification template matching the target identification.

1 . A method for information detection, comprising:
 determining key point information in a target identification from a target image based on a preset deep learning algorithm;   obtaining an image of the target identification from the target image according to the key point information; and   determining information of a preset field from the image of the target identification according to the image of the target identification and a preset identification template matching the target identification.   
     
     
         2 . The method according to  claim 1 , wherein the determining the key point information in the target identification from the target image based on the preset deep learning algorithm comprises:
 determining, based on data of a preset sample image, a relationship among key points corresponding to four right angles of an identification in the sample image and edges of the identification in the sample image and an avatar in the identification in the sample image, to construct a regression network model; and   determining the key point information in the target identification from the target image based on the constructed regression network model.   
     
     
         3 . The method according to  claim 2 , wherein the regression network model comprises a first convolutional layer, a first pooling layer, a first dropout layer, a second convolutional layer, a second pooling layer, a second dropout layer, a third convolutional layer, a third dropout layer, a first fully connected layer, and a second fully connected layer, wherein the first convolutional layer is connected to an input layer, the second fully connected layer is connected to an output layer, and layers are connected in an order of: the first convolutional layer, the first pooling layer, the first dropout layer, the second convolutional layer, the second pooling layer, the second dropout layer, the third convolutional layer, the third dropout layer, the first fully connected layer, and the second fully connected layer. 
     
     
         4 . The method according to  claim 3 , wherein the first dropout layer, the second dropout layer, and the third dropout layer are configured to increase preset noise information. 
     
     
         5 . The method according to  claim 4 , wherein the first fully connected layer comprises a plurality of cells for mapping a learned distributed feature representation into a space of the sample image. 
     
     
         6 . The method according to  claim 5 , wherein the second fully connected layer comprises eight cells, and the eight cells respectively correspond to the key points corresponding to the four right angles of the target identification and eight parameters. 
     
     
         7 . The method according to  claim 2 , wherein the determining the key point information in the target identification from the target image based on the constructed regression network model comprises:
 determining, based on the constructed regression network model, a region formed by the four right angles of the target identification from the target image; and   determining, based on the constructed regression network model, the key point information in the target identification from the determined region formed by the four right angles of the target identification.   
     
     
         8 . The method according to  claim 1 , wherein before determining the information of the preset field from the image of the target identification according to the image of the target identification and the preset identification template matching the target identification, the method further comprises:
 adjusting a size of the image of the target identification based on preset reference size information of the identification to obtain the image of the target identification matching the reference size information.   
     
     
         9 . A device for information detection, comprising:
 a processor; and   a memory configured to store instructions,   wherein the processor is configured to execute the instructions to:   determine key point information in a target identification from a target image based on a preset deep learning algorithm;   obtain an image of the target identification from the target image according to the key point information; and   determine information of a preset field from the image of the target identification according to the image of the target identification and a preset identification template matching the target identification.   
     
     
         10 . The device according to  claim 9 , wherein the processor is further configured to execute the instructions to:
 determine, based on data of a preset sample image, a relationship among key points corresponding to four right angles of an identification in the sample image and edges of the identification in the sample image and an avatar in the identification in the sample image, to construct a regression network model; and   determine the key point information in the target identification from the target image based on the constructed regression network model.   
     
     
         11 . The device according to  claim 10 , wherein the regression network model comprises a first convolutional layer, a first pooling layer, a first dropout layer, a second convolutional layer, a second pooling layer, a second dropout layer, a third convolutional layer, a third dropout layer, a first fully connected layer, and a second fully connected layer, wherein the first convolutional layer is connected to an input layer, the second fully connected layer is connected to an output layer, and layers are connected in an order of: the first convolutional layer, the first pooling layer, the first dropout layer, the second convolutional layer, the second pooling layer, the second dropout layer, the third convolutional layer, the third dropout layer, the first fully connected layer, and the second fully connected layer. 
     
     
         12 . The device according to  claim 11 , wherein the first dropout layer, the second dropout layer, and the third dropout layer are configured to increase preset noise information. 
     
     
         13 . The device according to  claim 12 , wherein the first fully connected layer comprises a plurality of cells for mapping a learned distributed feature representation into a space of the sample image. 
     
     
         14 . The device according to  claim 13 , wherein the second fully connected layer comprises eight cells, and the eight cells respectively correspond to the key points corresponding to the four right angles of the target identification and eight parameters. 
     
     
         15 . The device according to  claim 10 , wherein the processor is further configured to execute the instructions to:
 determine, based on the constructed regression network model, a region formed by the four right angles of the target identification from the target image; and   determine, based on the constructed regression network model, the key point information in the target identification from the determined region formed by the four right angles of the target identification.   
     
     
         16 . The device according to  claim 9 , wherein the processor is further configured to execute the instructions to:
 adjust a size of the image of the target identification based on preset reference size information of the identification to obtain the image of the target identification matching the reference size information.   
     
     
         17 . A non-transitory computer-readable storage medium having stored therein instructions that, when executed by a processor of a device, cause the device to:
 determine key point information in a target identification from a target image based on a preset deep learning algorithm;   obtain an image of the target identification from the target image according to the key point information; and   determine information of a preset field from the image of the target identification according to the image of the target identification and a preset identification template matching the target identification.