Target detection method based on incremental learning and automatic driving method

Abstract

The invention discloses a target detection method based on incremental learning. The method comprises the steps of obtaining an original target detection initial model; pre-training partial parameters of a feature extractor of the original target detection initial model to obtain a universal target detection feature extractor; performing structure expansion on the target detection feature extractor by adopting the detection head and the parameter mask to obtain an expanded target detection model; and performing actual target detection by adopting the expanded target detection model. The invention further discloses an automatic driving method comprising the target detection method based on incremental learning. According to the incremental learning-based target detection method and the automatic driving method provided by the invention, a new incremental learning target detection method is innovatively provided; through innovative addition of a detection head technology and a mask technology, incremental learning target detection is realized, and the method is high in accuracy, high in reliability and good in practicability.

Description

technical field [0001] The invention belongs to the field of computer image processing, and in particular relates to an incremental learning-based target detection method and an automatic driving method. Background technique [0002] With the development of economy and technology and the improvement of people's living standards, target detection technology has been widely used in people's production and life, bringing endless convenience to people's production and life. [0003] Current target detection methods often use deep learning models, and the learning method is mainly through stochastic gradient descent to fit the distribution of the given data. This form makes the performance of the deep learning model heavily dependent on the number of training samples at this stage, and each training process is coverage rather than incremental. Continuing training with new data on an already trained model creates the problem of catastrophic forgetting. This means that once the m...

AI Technical Summary

Problems solved by technology

However, this approach still cannot avoid the problem of forgetting, and it faces a dilemma-increased loss will limit the ability of the model to learn new tasks. The more new tasks learned, the more serious the forgetting will be, so that the performance of the model will be severely degraded.

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