Target object position detection method and device based on video stream, equipment and medium

Abstract

The invention relates to an artificial intelligence technology, and discloses a target object position detection method based on a video stream. The method comprises the steps of obtaining the video stream, and performing image framing on the video stream to obtain a framed image set; detecting a target area of the framed image set by using a target area detection model to obtain a target image set; identifying a target object position sequence of the target image set by using a target object position sequence identification model, and deleting target images corresponding to the abnormal target object position sequence from the target image set according to the target object position sequence to obtain a standard target image set; and performing image association on all the target images in the standard target image set, and identifying the position of the target object according to standard target images after image association. In addition, the invention also relates to a blockchaintechnology, and the video stream can be stored in a blockchain. The method can be applied to thyroid nodule position detection. According to the invention, the target object position detection accuracy based on the video stream can be improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, and in particular to a video stream-based target position detection method, device, electronic equipment and computer-readable storage medium. Background technique [0002] In recent years, neural network models have been widely used in medical image detection, such as the target detection model (Single ShotMultiBox Detector, referred to as SSD). Although the neural network model has a good effect on most image detection scenarios, due to the lack of large-scale doctor-labeled data in the medical field, when performing medical video detection, it often lacks the use of image context information in medical videos, thus It will affect the accuracy of medical video detection. For example, in the traditional video diagnosis of thyroid nodules, the thyroid is mainly scanned by cross-section and longitudinal section, and images of the segments that may appear in the entire scan...

AI Technical Summary

Problems solved by technology

Although the neural network model has a good effect on most image detection scenarios, due to the lack of large-scale doctor-labeled data in the medical field, when performing medical video detection, it often lacks the use of image context information in medical videos, thus Will affect the accuracy of medical video detection

For example, in the traditional video diagnosis of thyroid nodules, the thyroid is mainly scanned by cross-section and longitudinal section, and images of the segments that may appear in the entire scan video are taken, and then the location of thyroid nodules is judged. In the process, the use of contextual information of thyroid images in thyroid video is often lacking, which will affect the accuracy of thyroid video nodule position detection

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