Man-machine hybrid enhanced small target detection and tracking method and system

Abstract

The invention discloses a man-machine hybrid enhanced small target detection and tracking method and system, and the method comprises the steps: positioning a target region, and obtaining a small target region image; extracting specific image information of the small target area image to obtain a small target detail image; constructing a small target detection and tracking model based on wearableequipment, designing and training a lightweight neural network target detection model, combining a context information technology and a Kalman filtering algorithm, and using a small target detail image to optimize the detection and tracking capability of a small target; circularly obtaining new image information by utilizing the steps to serve as a training set for enriching the small target detection and tracking model, and performing migration training on the small target detection and tracking model by utilizing the updated training set to obtain a small target detection and tracking enhancement model. The system comprises an acquisition and identification module, a sensor module, a detection and tracking module and a communication module. According to the invention, the accuracy of small target detection and tracking in the image is improved.

Description

technical field [0001] The invention relates to small target detection and tracking in real-time images, in particular to a human-machine hybrid enhanced small target detection and tracking method and system. Background technique [0002] At present, real-time and high-precision target detection is an important and difficult technology that must be faced and solved in the field of computer vision. In recent years, the research and technology of neural network and deep learning technology in the field of computer vision have developed rapidly. Especially for the target detection task, the end-to-end real-time target detection neural network model has been realized, which is far superior in real-time and accuracy. traditional object detection methods. However, when applied in the actual military field, when faced with small platforms with limited computing power such as wearable devices, the existing deep network model cannot effectively detect small-scale targets after light...

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