A visual target detection and labeling method

Abstract

The present invention discloses a visual target detection and labeling method. The method includes: an image inputting step, to input an image to be detected; a candidate region extracting step, to extract a candidate window as the candidate region from the image to be detected using selectively search algorithm; a feature description extracting step, to perform feature description on the candidate region using a pre-trained large-scale convolutional neural network and output the feature description of the candidate region; a visual target predicting step, to predict the candidate region based on the feature description of the candidate region using a pre-trained object detection module, to estimate regions having the visual target; and a position labeling step, to labeling the position of the visual target according to the estimated result. Experiments show that, compared with the mainstream week supervision visual target detection and labeling method, the present invention has a stronger ability to excavate positive samples and a more general application prospect, and is suitable for visual target detection and automatic labeling tasks on the large-scale data set.

Description

technical field [0001] The invention relates to the technical field of object detection in computer vision, in particular to a visual target detection and labeling method based on weakly supervised learning. Background technique [0002] Object detection and automatic location labeling in images is a basic problem in the field of computer vision, and it is also one of the core issues to be studied in this field. Object detection in an image is to answer the question of what is where given a test image. Object detection is widely used in many other vision research problems, such as object recognition, pedestrian detection, face detection, foreground detection in surveillance scenes, motion tracking, behavior recognition and analysis, etc. [0003] General object detection needs to be given a database of marked object circumscribed rectangles in order to use purely supervised object detection models based on histogram of gradient orientation (HOG) and deformable part model (D...

AI Technical Summary

Problems solved by technology

Many traditional multi-instance learning algorithms rely heavily on kernel learning or distance metric-based learning frameworks, and use highly complex optimization algorithms such as heuristic algorithms, quadratic programming, and integer programming. Efficiently applied to datasets

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