Two-channel convolution neural network semantic segmentation method sensitive to small targets

Abstract

The invention discloses a dual-channel convolution neural network semantic segmentation method sensitive to small targets. The method comprises the following steps: a Caffe depth learning frame is used to build a non-weighted learning network and a weighted learning network; for the two-channel network, the corresponding semantic segmentation model is obtained by two-stage training. The output scoring charts of two channels are obtained by two semantic segmentation models, and the output scoring charts of two channels are fused by different model fusion algorithms, and the optimal model fusionalgorithm is selected according to the specific evaluation index. The test image is segmented according to the semantic segmentation model and the selected optimal model fusion algorithm. The invention can ensure that on the premise that the overall segmentation accuracy of the data set is better, the invention is more sensitive to the small target area existing in the image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a small target-sensitive dual-channel convolutional neural network semantic segmentation method. Background technique [0002] Image semantic segmentation is one of the three major tasks of computer vision. Its goal is to classify each pixel in the image and obtain a semantic segmentation map of the image. From the perspective of traditional image segmentation, image semantic segmentation is to segment the image into multiple regions at the semantic level, and then assign appropriate category labels to each region. At present, semantic segmentation has a wide range of applications in autonomous driving, real-time road monitoring, automatic virtual fitting, and medical disease systems. Before the rise of deep learning, the main method of semantic segmentation was to use the conditional random field model to build a probability graph model. In recent years, du...

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Problems solved by technology

[0005] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide a small target-sensitive dual-channel convolutional neural network semantic segmentation method, which fuses the small target-sensitive neural network segmentation results with the standard neural network segmentation results, Solve the problem of segmenting small-sized objects and other object accuracy imbalances

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