Patent ID: 11954917
Assignee: SHANDONG KAILIN ENVIRONMENTAL PROTECTION EQUIPMENT CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC B  G

Claim 0:
1. A method of segmenting an abnormal robust for complex autonomous driving scenes, comprising following steps:
S1, a segmentation module
firstly, taking an input image as an input of a segmentation network to obtain a semantic feature map of a middle layer;
secondly, quantifying, based on two discrete metrics of an entropy Ex for softmax and a distance Dy for softmax, an uncertainty of the semantic feature map;
S2, a synthesis module
directly comparing, through a model, high-level semantic information on images by introducing a perceptual loss between an original input image and a synthesized image, rather than merely utilizing low-level visual features such as colors and textures;
S3, a difference module
distinguishing, through a training network, the original input image from the synthesized image, and associating a generated feature map with an uncertainty feature map pixel by pixel; eventually; decoding, through a decoder, feature maps, and performing, based on a FPN, high-level skip connections on the feature maps respectively to eventually obtain segmentation prediction results of abnormal objects in complex scenes;
S4, a model training module
overwriting, by marking a void class in a Ground Truth image as an abnormal class, objects that do not belong to a training class,
wherein the model training module performs iterative trainings continuously based on a constructed loss function until training rounds or training accuracy reaches a set threshold, and save a network model with a minimum loss value; wherein considering that the abnormal objects do not belong to a class in a training set, the model training module is capable of performing a robust detection on the abnormal objects in complex real-world scenes on a premise of ensuring that the model does not overfit specific objects in non distributed data during a training process; and
S5, an integrated prediction module
integrating, by using weighted average values, prediction results from different inputs, wherein a size of an optimal weight is automatically selected by a grid searching, wherein the segmentation module, the synthesis module, the difference module, the model training module, and the integrated prediction module are executed by a hardware processor;
wherein in S1, a difference value between two distances is used to understand a false segmentation prediction, and the two distances are defined respectively as follows:, E
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where p(c) represents a softmax probability of a c class.