Image processing method and apparatus, medium, device and driving system

An image processing apparatus, comprising:

• one or more processors, and a non-transitory storage medium in communication with the one or more processors, the non-transitory storage medium configured to store program instructions, wherein, when executed by the one or more processors, the instructions cause the apparatus to perform:
• performing corresponding adjustment to a current environmental image to obtain a plurality of current environmental feature images, according to perception target features of each perception target feature class in a plurality of predetermined perception target feature classes, and a pixel requirement of a neural network image perception model corresponding to each perception target feature class in a plurality of predetermined neural network image perception models;
• perceiving a perception target in each current environmental feature image to obtain a plurality of perception results, by using the corresponding neural network image perception model, and,
• fusing the plurality of perception results to obtain perception result data of the current environmental image;
• wherein the plurality of perception target feature classes are in one-to-one correspondence with the plurality of neural network image perception models.

A computer readable storage medium, storing a computer instruction wherein the computer instruction is executed to enable a computer to perform the image processing method, which comprises:

• according to perception target features of each perception target feature class in a plurality of predetermined perception target feature classes, and a pixel requirement of a neural network image perception model corresponding to each perception target feature class in a plurality of predetermined neural network image perception models, performing corresponding adjustment to a current environmental image to obtain a plurality of current environmental feature images;
• by using the corresponding neural network image perception model, perceiving a perception target in each current environmental feature image to obtain a plurality of perception results, and,
• fusing the plurality of perception results to obtain perception result data of the current environmental image;
• wherein the plurality of perception target feature classes are in one-to-one correspondence with the plurality of neural network image perception models.