Model training method and device, target detection method and device, equipment and storage medium

Abstract

The embodiment of the invention relates to a detection model training method and device, equipment and a storage medium, and aims to improve the generalization ability of a model. The method comprises the steps of obtaining a plurality of source domain sample images and a plurality of target domain sample images, wherein each source domain sample image comprises annotation box information of a pre-marked source domain object; inputting the source domain sample image and the target domain sample image into a feature extraction model in pairs to obtain a first feature map and a first detection frame of the source domain sample image and a second feature map and a second detection frame of the target domain sample image; judging the domain category of each pixel point in each feature map to obtain a plurality of first judgment results; judging the domain category of each detection box to obtain a plurality of second judgment results; and updating the feature extraction model according to the first detection box and annotation box information and according to the plurality of first judgment results and the plurality of second judgment results.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular, relate to a model training method, a target detection method, a device, a device, and a storage medium. Background technique [0002] With the development of image processing technology and the improvement of the computing power of hardware equipment, in order to enable the machine to intelligently realize functions such as detection, classification, and prediction, technicians collect a large number of sample pictures and mark the sample pictures. The sample images are used to train the built preset model to obtain a model with desired functions. During this period, the quality of model training usually depends on the structure and type of the preset model built by the technician, and also depends on the way the technician selects and marks the sample pictures. [0003] Taking target detection based on road scenes as an example, technician...

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Problems solved by technology

[0004] However, when the preset model is trained in the above way, the generalization ability of the final model is poor

For example, technicians select a large number of pictures of country A's road field as sample pictures to train the target detection model, and the obtained expected model has better detection and classification capabilities for each object in the picture of country A's road field, but for It is difficult to achieve accurate detection and classification of various objects in the pictures of the road field in country B, and the adaptability of the model is low

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