Neural network-based classification method and classification device thereof
AssigneeIndustrial Technology Research Institute
InventorMao-Yu Huang
Life of this patent
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Abstract
A neural network-based classification method, including: obtaining a neural network and a first classifier; inputting input data to the neural network to generate a feature map; cropping the feature map to generate a first cropped part and a second cropped part of the feature map; inputting the first cropped part to the first classifier to generate a first probability vector; inputting the second cropped part to a second classifier to generate a second probability vector, wherein weights of the first classifier are shared with the second classifier; and performing a probability fusion on the first probability vector and the second probability vector to generate an estimated probability vector for determining a class of the input data.
Claims23
A neural network-based classification device, comprising:
- a storage medium, storing a plurality of modules;
- and
- a processor coupled to the storage medium, the processor accessing and executing the modules of the storage medium, wherein the modules comprise:
- a neural network, generating a feature map according to input data;
- a cropping layer, cropping the feature map to generate a first cropped part and a second cropped part of the feature map;
- a first classifier, generating a first probability vector according to the first cropped part;
- a second classifier, generating a second probability vector according to the second cropped part, wherein weights of the first classifier are shared with the second classifier;
- and
- a fusion layer, performing a probability fusion on the first probability vector and the second probability vector to generate an estimated probability vector for determining a class of the input data.
A neural network-based classification method, comprising:
- obtaining a neural network and a first classifier;
- inputting input data to the neural network to generate a feature map;
- cropping the feature map to generate a first cropped part and a second cropped part of the feature map;
- inputting the first cropped part to the first classifier to generate a first probability vector;
- inputting the second cropped part to a second classifier to generate a second probability vector, wherein weights of the first classifier are shared with the second classifier;
- and
- performing a probability fusion on the first probability vector and the second probability vector to generate an estimated probability vector for determining a class of the input data.
A neural network-based classification device suitable for classifying appearance defects of objects, the classification device comprising:
- an automated optical inspection equipment, obtaining image data of the objects;
- and
- a server comprising a storage medium and a processor, the server being coupled to the automated optical inspection equipment and receiving the image data, wherein
- the storage medium stores a plurality of modules, and
- the processor is coupled to the storage medium and accesses and executes the modules of the storage medium, the modules comprising:
- a neural network, generating a feature map according to the image data;
- a cropping layer, cropping the feature map to generate a first cropped part and a second cropped part of the feature map;
- a first classifier, generating a first probability vector according to the first cropped part;
- a second classifier, generating a second probability vector according to the second cropped part, wherein weights of the first classifier are shared with the second classifier;
- and
- a fusion layer, performing a probability fusion on the first probability vector and the second probability vector to generate an estimated probability vector for determining a class of the image data.
Dependencies are inferred from the claim text.