Incremental small sample target detection method and system based on weight generation

Abstract

The invention belongs to the field of computer vision, particularly relates to an incremental small sample target detection method and system based on weight generation, and aims to solve the problems that an existing target detector lacks the capacity of small sample rapid learning and incremental learning, is high in dependency on label data and does not have openness. The method comprises the following steps: performing detector supervision training through basic category data; obtaining weights of scale perception and centrality perception of the basic category target detector, and generating a basic category response; generating a new category weight in combination with the basic category response; performing fine tuning training of the basic category target detector in combination with the new category data; and realizing incremental small sample target detection through the obtained target detectors of the basic category and the new category. According to the method, scale and centrality perception is combined, regional features are more representative, target positioning is more accurate, the model can obtain better overall performance in incremental learning, and detection efficiency, accuracy and precision are high.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to an incremental small sample target detection method and system based on weight generation. Background technique [0002] As an essential step in many computer vision tasks, object detection has attracted extensive attention from academia and industry in recent years. With the help of deep neural networks, the field of object detection has made great progress. However, deep neural networks are highly dependent on huge training data and manual labeling that consumes a lot of manpower, and in many practical situations, it is not easy for people to get massive labels. At the same time, when faced with a data stream that may increase new detection requirements over time, most object detectors based on deep neural networks lack the ability of fast learning and incremental learning with small samples. [0003] Recently, several studies have proposed incremental / few-shot lear...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned problems in the prior art, that is, the existing target detector lacks the ability of small-sample rapid learning and incremental learning, has a strong dependence on label data, and the detector does not have annotation efficiency and openness, the present invention provides An incremental small-sample object detection method based on weight generation is proposed, which includes:

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