Automatic annotation method and device of image, electronic equipment and storage medium

Abstract

The invention provides an automatic annotation method and device of an image. The method comprises the steps of obtaining a to-be-annotated image; target feature detection is performed on the to-be-labeled image, a first position of a target feature in the to-be-labeled image is identified, and a labeling box corresponding to the target feature is determined at the first position according to a preset labeling rule; determining an annotation strategy according to the distribution type of the target features in the to-be-annotated image, and selecting a target annotation box from annotation boxes determined according to a preset annotation rule according to the annotation strategy; and labeling the target feature in the to-be-labeled image by adopting the target labeling box. According to the method, the target features can be accurately and automatically marked in the image, the cost is low, the speed is high, and the precision is high.

Description

technical field [0001] The present application belongs to the technical field of image recognition, and in particular relates to an image automatic labeling method, device, electronic equipment and storage medium. Background technique [0002] Computer vision is one of the main driving forces and necessary means to promote industrial automation and intelligence. Defect detection of objects in the industry is a branch technology of target detection tasks in the field of computer vision. Machine learning and deep learning methods are commonly used to detect object defects. Almost all object products in industrial production need to undergo quality inspection. Object defects are usually some defects found in the quality inspection process that will affect the quality inspection results of object products, such as some scratches, stains, plaques, wear, debris, etc. Due to the diversity of object products in industrial production, object defects are also diverse. In the process...

AI Technical Summary

Problems solved by technology

At present, the existing machine learning and deep learning methods for object defect detection require a lot of manpower and time costs in the early data preparation stage to manually label tens of thousands of defective pictures to provide a large number of The labeled data is used as training samples for machine learning and deep learning. This labeling method is costly, slow, and easily affected by the subjective factors of the labeler. The accuracy required

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