Visual small target automatic labeling method and device and electronic equipment

Abstract

The embodiment of the invention provides a visual small target automatic labeling method and device and electronic equipment, and belongs to the technical field of data processing, and the method comprises the steps: classifying video frames containing a visual small target, obtaining a first type of video frames and a second type of video frames, and enabling one or more second type of video frames to be included between each two adjacent first type of video frames; respectively executing target detection and tracking processing on the first type of video frames and the second type of video frames in a sequential mode to obtain a first detection result; performing target detection and tracking processing on the first type of video frames and the second type of video frames in an invertedorder mode to obtain a second detection result; and performing fusion processing on the first detection result and the second detection result by adopting a maximum suppression mode to obtain a finallabeling result for automatically labeling the small target. Through the processing scheme disclosed by the invention, the automatic labeling efficiency of the visual small target can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular to a method, device and electronic equipment for automatic marking of small visual objects. Background technique [0002] Data labeling is a kind of labor-intensive work with high manual cost. It mainly relies on human knowledge to make some marks for visual data, which is academically called Ground Truth (true value). The artificial intelligence algorithm is trying to build a mathematical model, which can simulate human knowledge through learning, so that artificial intelligence can make judgments similar to human intelligence. [0003] Obtaining data annotation often requires a huge price. Every skilled data annotation engineer can only mark hundreds or even thousands of annotation boxes every day with simple tools. The data required for artificial intelligence training is indeed extremely huge. [0004] The quality of data annotation will also directly aff...

AI Technical Summary

Problems solved by technology

When humans are engaged in such boring and mechanical work, fatigue is inevitable, which will seriously affect the quality of data labeling

[0005] There is a contradiction between the demand for massive data for artificial intelligence algorithm training and the low efficiency of human labeling, and there is a contradiction between mechanical and boring work content and high-quality requirements for work

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