Automatic driving processing method and apparatus based on scene segmentation and computing device

Abstract

The invention discloses an automatic driving processing method and apparatus based on scene segmentation and a computing device. The method comprises the following steps: a current frame image in thevideo shot and/or recorded via an image collection device is obtained in real time during vehicle driving processes, the current frame image is input into a second neural network to obtain a scene segmentation result corresponding to the current frame image, the second neural network is obtained after output data of at least one intermediate layer of the pre-trained first neural network is subjected to guiding and training operation, and the first neural network is greater than the second neural network in terms of layer quantity. According to a scene segmentation result, a driving route and/or a driving instruction is determined; autonomous driving control is exerted on a vehicle is according to the determined driving route and/or driving instruction. Via use of the automatic driving processing method and apparatus based on scene segmentation and the computing device, the neural network with a small number of layers after training operation is used for achieving fast and accurate calculation so as to obtain the scene segmentation result, the scene segmentation result used for accurately determining the driving route and/or the driving instruction, and improvement of automatic driving safety can be facilitated.

Description

technical field [0001] The present invention relates to the field of deep learning, in particular to a scene segmentation-based automatic driving processing method and device, and computing equipment. Background technique [0002] In the existing technology, image scene segmentation processing is mainly based on the fully convolutional neural network in deep learning. These processing methods use the idea of ​​transfer learning to migrate the network obtained through pre-training on a large-scale classification data set to the image The segmentation network is trained on the segmentation data set to obtain the segmentation network for scene segmentation, and then the segmentation network is used to segment the scene of the image. [0003] Autonomous driving based on scene segmentation has high requirements on the timeliness and accuracy of scene segmentation to ensure the safety of autonomous driving. The fully convolutional neural network used in the prior art often has mu...

AI Technical Summary

Problems solved by technology

However, the calculation speed of the multi-layer intermediate layer will be slow, and the scene cannot be quickly divided, and the status of the own vehicle on the way cannot be quickly obtained.

When using a neural network with fewer intermediate layers, the calculation speed is faster due to the fewer intermediate layers, but limited by the number of layers, it may result in limited computing power, poor fitting ability, and inaccurate results. question

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