A Gesture Recognition Method Based on Head Lightweight Mask Scoring R-CNN

Abstract

The invention relates to a gesture recognition method based on Head lightweight Mask Scoring R-CNN, which introduces a lightweight position-sensitive score map and position-sensitive RoIAlign after the output feature map of the original Mask Scoring R-CNN backbone network, so that the Head structure The number of input RoI channels becomes very small, and the two consecutive fully connected layers in the Head structure are changed to a single fully connected layer to reduce the amount of calculation. The present invention uses DetNet59 combined with FPN as the backbone network, so that the extracted multi-scale feature map can contain rich semantic information and position information at the same time, and can adapt to objects of various sizes for detection. The average precision of the improved instance segmentation model of the invention is significantly improved, the number of model parameters is effectively reduced, and the training and detection speed of the model are effectively improved.

Description

technical field [0001] The invention relates to a gesture recognition method based on Head lightweight Mask Scoring R-CNN, belonging to the fields of computer vision and deep learning. Background technique [0002] Gesture recognition is an important branch in the field of computer vision. Its core is to use 'machine eyes' to replace human eyes to recognize hand gestures in images or video capture devices, and input the captured images or videos into visual algorithms for calculation. Finally got the hand information. There are many kinds of vision algorithms mentioned here, such as traditional image processing methods and deep learning methods in recent years. Before the advent of deep learning, traditional image processing and machine learning methods could not perform a simple image classification task well, but the emergence of deep learning has made it possible for computers to reach human level. In fact, the emergence of AlphaGo has proved that in some fields, comput...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the precision of the gesture detector in the first stage is not high enough to make a detailed prediction on the gesture mask; the speed of the gesture detector in the second stage is too slow

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