PATENT CLAIM ANALYSIS

Application Number: 16049026
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2019-03
Patent Classification: ["701", "028000"]

Abstract:
The disclosure discloses a method and apparatus for identifying a traffic light. An embodiment of the method comprises: zooming a to-be-processed image acquired by an image acquisition device by at least one preset ratio to obtain at least one zoomed image; inputting the at least one zoomed image into a pre-trained convolutional neural network to obtain location information and category information of a traffic light corresponding to each zoomed image of the at least one zoomed image, wherein the convolutional neural network is used for retrieving location information and category information of a traffic light displayed in an image; and analyzing the obtained location information and category information to generate at least one candidate traffic light identification result, and fusing the generated candidate traffic light identification result to generate a traffic light identification result corresponding to the to-be-processed image. The embodiment improves the accuracy in identifying a traffic light.

Claim (Index 12):
The apparatus for identifying a traffic light according to  claim 11 , wherein the obtaining the convolution neural network through training using a machine learning method based on a preset image set, a preset regression loss function, a preset classification loss function and a backpropagation algorithm comprises:\n randomly initializing a parameter of a pre-established initial convolutional neural network using a Gauss distribution; executing following training: generating a training sample carrying an annotation based on the preset image set, the annotation containing location information and category information of a traffic light; inputting the training sample into the initial convolutional neural network for forward propagation layer by layer, to obtain the location information and the category information outputted by the initial convolutional neural network; determining a value of the preset regression loss function and a value of the preset classification loss function based on the obtained location information and category information, and the location information and the category information contained in the annotation; calculating a gradient of a parameter of each layer of the initial convolutional neural network layer by layer using a chain rule and a backpropagation algorithm, and based on a sum of the value of the regression loss function and the value of the classification loss function; and updating a parameter of the initial convolutional neural network based on the calculated gradient; determining a number of times of executing the training, and continuing, in response to determining the number of times of the executing the training being less than a first preset number of times, to execute the training of the initial convolutional neural network after updating the parameter; and performing statistics, in response to determining the number of times of the executing the training being equal to the first preset number of times, on a parameter obtained in a finally second preset number of times of executing the training, determining a target parameter, and replacing a parameter of the initial convolutional neural network with the target parameter to obtain a trained convolutional neural network, wherein the second preset number of times is less than the first preset number of times.

Metadata:
- Claim Count in Document: 6.0
- Percentile: 95.0
- Lexical Diversity: 2.49206
- Patent Class: 701.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['16001855', '15721881', '14723373', '14726172', '15470360']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4453227816109923
- 35 USC 102 Novelty (BERT): 0.4878403299431529
- Combined Prediction Score: 0.4495745364442084
- Mean Citation Score: 182.341522
- Max Citation Score: 195.85567
- Similarity Product: 131.1510990499556

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 0
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test