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

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...

AI Technical Summary

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 according to the setting frame to obtain the final target detection frame, so this method requires more Large computing power to support, and the model is more complex

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