An image recognition method and system based on lightweight convolutional neural network

Abstract

The invention discloses an image recognition method and system based on a lightweight convolutional neural network, comprising: loading a pre-trained deep convolutional neural network model for image recognition; using a kernel-based sparse method to calculate the model The cropping factor of the middle convolutional layer, the convolutional layer is cropped, and then the cropping factor of each convolution kernel in the uncropped convolutional layer is calculated, and the convolution kernel is cropped; for the convolutional neural network after the cropping is completed model, the standard convolution method is replaced by the convolutional and separable group convolution method, and the weight coefficient matrix of the convolution kernel is quantized and encoded to obtain a lightweight convolutional neural network model; the image dataset is used to quantify the lightweight model. For training, input the image to be recognized into the trained lightweight convolutional neural network model for image recognition. The light-weight image recognition model disclosed by the invention can be loaded on a terminal with limited computing power and storage resources, and has a relatively broad application prospect.

Description

technical field [0001] The invention relates to the field of image processing and pattern recognition, in particular to an image recognition method and system based on a lightweight convolutional neural network. Background technique [0002] With the continuous development of deep learning in the field of target recognition and detection, following AlexNet, VGG, GoogleNet, ResNet and other networks have developed deeper network layers in order to seek better detection accuracy. However, in many current applications, such as automatic driving, fatigue detection, robots, etc., limited by the integration of equipment and processing speed, model compression research emerges as the times require. New deep neural networks are constantly proposed; related researchers use increasing convolution layers to increase The number of convolution kernels and other methods can be used to extract the deep-level features of the detection target; although the deep network model has excellent pe...

AI Technical Summary

Problems solved by technology

However, many current applications, such as automatic driving, fatigue detection, and robots, are limited by integrated equipment and processing speed, and model compression research has emerged as the times require. New deep neural networks have been proposed; The number of convolution kernels and other methods are used to extract the deep-level features of the detection target; although the deep network model is superior in many problems, it is restricted by time and space in practical application, and the large and deep network model has a large amount of computation. Huge, even with the help of graphics processors, it is difficult to embed and develop on devices with limited computing resources and storage resources, and it is difficult to meet the needs of many scenarios in daily life in time; high-performance computers have high production and maintenance costs and are not suitable for A large number of popularization and promotion; for example, traditional pedestrian detection equipment (such as camera monitoring head) uploads the recorded video to a remote large-scale server for data processing. Due to the influence of bandwidth and transmission delay, it is impossible to effectively detect target pedestrians in real time. Therefore, the application in some special occasions such as arresting suspects and finding lost children is relatively limited; therefore, a lightweight convolutional neural network is designed so that it can be embedded and developed on mobile terminals with limited computing and storage capabilities, making it Being able to effectively recognize image targets in real time will break through the application limitations of deep network models to a certain extent, and has broad application prospects

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