Unsupervised domain adaptive target detection method based on center alignment and relationship significance

Abstract

The invention discloses an unsupervised domain adaptive target detection method based on center alignment and relation significance, and the method comprises the steps: in a training stage, generatinga corresponding target region proposal for images of a source domain and a target domain through a detector; performing relation modeling on the target area proposal and the category center, and updating the category center and the target area proposal; shortening the distance of each category between the target domain and the source domain by utilizing the category center obtained by updating, so that the distances between different categories of the target domain are increased by means of source domain information; and after the training is finished, directly carrying out classification detection on the target domain image. According to the method, the category center does not need to be independently calculated, and the category center and the target area proposal are put into the graph to be updated together, so that the model can be trained end to end; when the category centers are aligned, the inter-category difference of the target domain can be expanded while the distributiondifference of the source domain and the target domain is reduced, so that the target domain is effectively classified.

Description

technical field [0001] The invention relates to the technical field of target detection, in particular to an unsupervised domain adaptive target detection method based on center alignment and relationship saliency. Background technique [0002] As a basic problem in the direction of computer vision, target detection has made rapid progress in recent years driven by deep learning. However, target detection faces a serious problem. When the distribution of test data is different from that of training data, the detection performance will seriously decline. This problem is called "domain shift", where the data domain used to train the model is called the source domain, and the test data domain is called the target domain. One way to solve this problem is to collect and label the data of the target domain. Then it is trained based on the target domain data. However, manual data labeling consumes a lot of manpower and material resources, especially for tasks such as target detecti...

AI Technical Summary

Problems solved by technology

This method has also begun to be applied in the field of target detection. However, the current method basically follows the experience of image recognition and does not fully consider the characteristics of target detection itself. Exploiting the relationship o

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