Model training method, image detection method and detection device
A model training and image detection technology, applied in the field of image processing, can solve the problems of slowing down the convergence speed of the model and high computational complexity, and achieve the effects of increasing the operating speed, reducing the complexity, and reducing the loss
Active Publication Date: 2021-07-13
SENSLAB INC
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[0006] The purpose of the present invention is to provide a model training method, an image detection method and a detection device to solve the problem that the existing loss function contains a log operation unit, the calculation complexity is high, and the convergence speed of the model is slowed down
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[0072] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are part of the present invention Examples, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the usual meanings understood by those skilled in the art to which the present invention belongs. As used herein, "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other el...
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The invention provides a model training method, and the method comprises the steps: constructing a product type Focal loss function, carrying out the model training of a neural network model through employing the product type Focal loss function, and outputting a trained neural network model, wherein the construction method of the product type Focal loss function comprises the following steps: setting a weight value so as to solve the problems that the existing loss function contains a log operation unit, so the calculation complexity is relatively high and the model convergence speed is slowed down; setting a sample proportion balance factor alpha, and constructing the product type Focal loss function through W and alpha, so the calculation complexity is reduced, the operation speed is improved, the problem that the power series multiplied by the contribution of the wrongly classified target individuals to the loss function is increased is solved, the power series reduction of the contribution of the correctly classified target individuals to the loss function is also considered, and the product type Focal loss function reflects the overall judgment condition of the feature map. The invention provides an image detection method and a detection device.
Description
technical field [0001] The invention relates to the technical field of image processing, in particular to a model training method, an image detection method and a detection device. Background technique [0002] Human figure detection refers to detecting whether there is a human figure in the image, extracting features from the human figure image, and detecting the human figure through the extracted features. Humanoid detection is an important research topic in computer vision, and it is widely used in intelligent video surveillance, vehicle assisted driving, intelligent transportation, intelligent robots and other fields. The mainstream humanoid detection methods are divided into statistical learning methods based on artificial image features and deep learning methods based on artificial neural networks. Deep learning methods include loss functions, which, as a means of measuring the inconsistency between model predictions and real values, are crucial for automatic paramete...
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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/241
Inventor 龚向阳
Owner SENSLAB INC