A real-time scene image semantic segmentation method based on lightweight network

Abstract

The invention discloses a real-time scene image semantic segmentation method based on a lightweight network, comprising the following steps: S1, training a lightweight network classification model according to a scene image data set; S2, constructing a deep convolution neural network model based on a lightweight network classification model; S3, inputting the training data of the scene image dataset into a depth convolution neural network, output a predicted image, comparing the predicted image with a semantic label image of the scene image data set, and calculating a cross entropy loss as anobjective function to obtain a trained image semantic segmentation model; S4, inputting the real-time scene image to be tested into the image semantic segmentation model to obtain the image semanticsegmentation result. By taking the modified MobileNetV2 as a basic network, the invention can efficiently extract image features, and in the upsampling process, a quick connection block is used, so that the parameter utilization is more efficient, and the speed of the semantic segmentation model is further improved.

Description

technical field [0001] The invention belongs to the technical field of image semantic segmentation, and in particular relates to a real-time scene image semantic segmentation method based on a lightweight network. Background technique [0002] Scene semantic segmentation should belong to the application of image semantic segmentation on scene images. Scene semantic segmentation plays a vital role in subsequent computer vision tasks, such as the distinction between pedestrians and vehicles in unmanned driving. [0003] Semantic segmentation is an important part of many practical application scenarios, such as machine vision, autonomous driving, and mobile computing. Accurately understanding the surrounding scenes is very important for practical application decisions. Therefore, the runtime is the key to evaluating the semantic segmentation system in actual application scenarios. key factor. At present, the development of deep convolutional neural networks has made remarkabl...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above shortcomings in the prior art, the lightweight network-based real-time scene image semantic segmentation method provided by the present invention solves the problem of slow image semantic segmentation speed in the prior art

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