Obstacle recognition model training method and device, electronic equipment and storage medium

Abstract

The invention provides an obstacle recognition model training method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The obstacle recognition model training method comprises the steps of obtaining a newly added sample road image and a pre-trained initial obstacle recognition model, wherein the initial obstacle recognition model comprises a historical detection branch network for detecting a first type of obstacle; determining a second type of obstacle corresponding to the newly added sample road image, and constructing a corresponding target detection branch network for detecting and identifying the second type of obstacle in the initial obstacle identification model; and fixing network weight information corresponding to the historical detection branch network, and training the initial obstacle recognition model according to the newly added sample road image to obtain a trained incremental obstacle recognition model. According to the technical scheme of the embodiment of the invention, in the training process of the incremental obstacle recognition model, the reconstruction of the whole obstacle recognition model and the reduction of the recognition efficiency of old obstacles can be avoided.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, in particular, to an obstacle recognition model training method, an obstacle recognition model training device, an incremental obstacle recognition model, an obstacle recognition method, electronic equipment, and a computer-readable storage medium. Background technique [0002] With the deepening of research in the field of autonomous driving and the rapid development of artificial intelligence technology, autonomous driving has become a research hotspot, and the target detection algorithms for autonomous driving are becoming more and more diverse. [0003] However, when training an obstacle recognition model for detecting new types of obstacles in a related target detection algorithm, either it is necessary to retrain the entire obstacle recognition model, or it is time-consuming and computationally expensive to train the obstacle recognition model. The large selective s...

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Problems solved by technology

[0003] However, when training an obstacle recognition model for detecting new types of obstacles in a related target detection algorithm, either it is necessary to retrain the entire obstacle recognition model, or it is time-consuming and computationally expensive to train the obstacle recognition model. The large selective search network and classification network make the training process of the obstacle recognition model more complicated, and the iteration period of the obstacle recognition model is longer, and the trained obstacle recognition model has a low recognition efficiency for the original category of obstacles

[0004] Therefore, how to avoid retraining the entire obstacle recognition model in the process of training the obstacle recognition model used to detect new types of obstacles, and avoid the problem of low recognition efficiency for old types of obstacles, has become an automatic detection algorithm. Direction of the research

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