Object detection model training method and device, object detection model detection method and device, equipment and medium

Abstract

The invention discloses an object detection model training method and device, an object detection model detection method and device, equipment and a medium. The object detection model training methodcomprises the steps of obtaining an initial network model, wherein the initial network model comprises at least two detection sub-modules for objects in different scale intervals; adjusting a corresponding data augmentation strategy according to the scale interval; and augmenting the training sample images by using the adjusted data augmentation strategy, and training the corresponding detection sub-modules in the initial network model by using the augmented training sample images according to the scale interval to obtain an object detection model. According to the method, the network complexity does not need to be increased, the data diversity is increased by adjusting the data augmentation strategy, and the detection rate of the object detection model obtained by training can be effectively improved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an object detection model training method, detection method, device, equipment and medium. Background technique [0002] As an important function in the field of computational vision, target detection has extensive research value and application value. With the development of deep learning, the performance of target detection has been greatly improved, and it has been able to meet the needs of most scenarios. In the application process, there are often cases where the detection rate of objects of a certain scale is high and the detection rate of objects of other scales is low, and it cannot be guaranteed that objects of any scale can be effectively detected. In order to solve this problem, existing target detection methods consider introducing the same semantic features for models of objects of different scales or designing complex loss functions to focus on obje...

AI Technical Summary

Problems solved by technology

In the application process, there are often situations where the detection rate of objects of a certain scale is high and the detection rate of objects of other scales is low, and it cannot be guaranteed that objects of any scale can be effectively detected.

In order to solve this problem, existing target detection methods consider introducing the same semantic features for models of different scale objects or designing complex loss functions to focus on objects with low detection rates. The introduction of the same semantic features of the model often requires complex network design, which will increase the complexity of the target detection algorithm, and designing complex functions will make the target detection algorithm focus on objects of a specific scale, which often reduces to a certain extent. Detection rate of other scale objects

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