Method for automatically establishing artificial intelligent image recognizing training materials and annotation files

Abstract

The invention discloses a method for automatically establishing artificial intelligent image recognizing training materials and annotation files. The method is characterized by comprising the following steps of S1, designing a 3D model and scene, and conducting three-dimensional modeling through CAD type software numbers; S2, synthesizing training images, reading the 3D model, materials and background information in the step S1 through tool software, conducting simulating camera shooting on the 3D model, materials and background to obtain shot images with different distances, angles, time durations and scenes, combining the images obtained through simulation and the object materials, synthesizing and outputting the training images, and recording the model space positions of all focused objects under current states during outputting; S3, establishing the object annotation files, and conducting annotation on the model space position information, recorded in the step S3, of all the focused objects to generate the annotation files; S4, storing the annotation files. By means of the method, the high-quality training materials and annotation files can be rapidly generated.

Description

Technical field [0001] The invention relates to a method for automatically creating artificial intelligence image recognition training materials and annotation files, and belongs to the technical field of artificial intelligence. Background technique [0002] The artificial intelligence platform software is relatively mature, and can form a specific picture recognition model by automatically learning a large number of training materials (pre-labeled pictures). Then apply this model to identify whether there is an object of interest in the new picture, and describe the exact position of the object in the picture. However, the current difficulties in obtaining training materials are as follows: [0003] Difficult to shoot. The existing training photo data is usually collected by manual or aerial photography. Due to various restrictions on safety, distance, angle, shooting equipment, time, climate, etc., the number of training pictures formed is small, and it is difficult to form t...

AI Technical Summary

Problems solved by technology

[0005] overall low quality

In the shooting stage, it is difficult to accurately take a large number of photos that meet expectations, so the overall number of photos is relatively small and the time is long

The labeling stage mainly relies on manual work, but limited by physiological factors, after manual continuous labeling of multiple pictures, the labeling speed and accuracy will drop rapidly, which will affect the overall quality of the training picture data set finally generated by training.

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