Multi-task learning deep network model training and target detection method and device

A technology of multi-task learning and deep network, applied in the field of multi-task learning deep network model training and target detection, which can solve problems such as large computing power and complex models, and achieve the effect of increasing feature expression, improving detection efficiency and improving effect

Pending Publication Date: 2021-11-02
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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Problems solved by technology

However, this method requires a preset setting frame to detect whether there is a target in the setting frame, and if there is a target, adjust the prediction frame a

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  • Multi-task learning deep network model training and target detection method and device
  • Multi-task learning deep network model training and target detection method and device
  • Multi-task learning deep network model training and target detection method and device

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Embodiment Construction

[0055] Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0056] It should be noted that the definition of multi-task learning is a machine learning method that learns multiple related tasks together based on a shared representation. At the same time, multi-task learning is also a method of derivation and transfer learning. The main task uses the domain-related information possessed by the training signals of related tasks as ...

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Abstract

The invention provides a multi-task learning deep network model training and target detection method and device, and relates to the technical field of computer vision and deep learning. The specific implementation scheme is as follows: obtaining training data of multi-task learning in a target detection scene; inputting the image sample into a backbone network in a multi-task learning deep network model based on Anchor-Free to obtain a feature map output by the backbone network; inputting the feature map into a feature pyramid network in a multi-task learning deep network model to obtain a multi-scale feature map output by the feature pyramid network; inputting the multi-scale feature map into a Head network in a multi-task learning deep network model to learn each task, and obtaining a prediction result corresponding to each task output by the Head network; and training a multi-task learning deep network model according to the prediction result corresponding to each task output by the Head network and the label on the image sample corresponding to each task. According to the invention, the representation capability of the network can be improved.

Description

technical field [0001] The present disclosure relates to the field of artificial intelligence, specifically to the technical fields of computer vision and deep learning, which can be used in intelligent traffic scenarios, and in particular to a multi-task learning deep network model training and target detection method and device. Background technique [0002] Multi-task learning definition: Based on shared representation, a machine learning method that learns multiple related tasks together. At the same time, multi-task learning is a method of derivation and transfer learning. The main task uses the domain-related information possessed by the training signals of related tasks as a machine learning method to derive deviations to improve the generalization effect of the main task. [0003] In the current multi-task learning method, most detection tasks adopt the anchor base scheme, which enables the network to learn multi-level features through supervision information and los...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06F18/214G06F18/241G06F18/29
Inventor 杨喜鹏谭啸孙昊
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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