Sample acquisition and rapid labeling method with relatively fixed target state

Abstract

The invention discloses a sample acquisition and rapid labeling method with a relatively fixed target state, and the method comprises the following steps: firstly, arranging a camera for shooting a target; secondly, connecting a picture acquisition device in communication way with each camera and obtaining pictures shot by each camera; then, enabling the picture acquisition equipment to configure all target information, camera information, an associated target, a camera and camera preset position information; then, enabling the picture acquisition equipment to start an automatic snapshot tool to generate a sample picture and a pre-annotation file; and finally, manually checking the automatically captured sample pictures, carrying out batch secondary labeling on the pictures of which the target states are changed, and finally completing sample collection and rapid labeling. According to the method, the problems of difficulty in sample collection and time-consuming and labor-consuming sample labeling in sample training in the field of deep learning image recognition are solved, the samples can be quickly acquired and labeled, and the efficiency of image recognition research or engineering implementation is greatly improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence deep learning, in particular to a sample acquisition and fast labeling method with a relatively fixed target state. Background technique [0002] Image recognition technology based on artificial intelligence deep learning is developing rapidly, and has penetrated into various fields such as industry and residential life. Image recognition applications include classification, detection, segmentation, text recognition and other scenarios, in which classification and detection often rely on a large number of samples. Acquisition and accurate labeling of abnormal samples, but at present, image recognition has pain points such as small samples, uneven samples, susceptibility to interference, difficult sample collection, long sample labeling processing cycle, large environmental impact, and poor interpretability. At present, the algorithm of image recognition is relatively mature, and the research...

AI Technical Summary

Problems solved by technology

[0002] Image recognition technology based on artificial intelligence deep learning is developing rapidly, and has penetrated into various fields such as industry and residential life. Image recognition applications include classification, detection, segmentation, text recognition and other scenarios, in which classification and detection often rely on a large number of samples. Acquisition and accurate labeling of abnormal samples, but at present, image recognition has pain points such as small samples, uneven samples, susceptibility to interference, difficult sample collection, long sample labeling processing cycle, large environmental impact, and poor interpretability.

At present, the algorithm of image recognition is relatively mature, and the research direction is focused on parameter optimization, model acceleration, scheme design, etc. In addition, the recognition effect of image recognition basically depends on the training of a large number of accurately labeled samples in different environments. Existing labeling tools Although there are many open source projects including LabelImg, Labelme, RectLabel, etc., manual labeling is time-consuming and labor-intensive without exception. For scenes with complex environments, thousands of image labels and continuous iterative test scenarios are often required. Manual labeling is seriously restricted. With the effective development of image recognition work, if a large number of samples can be quickly obtained and accurate annotation can be completed quickly, it will greatly improve the research progress and engineering implementation efficiency in the field of image recognition

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