A real-time vehicle detection method based on micro-convolution neural network

Abstract

The invention discloses a vehicle real-time detection method based on a micro-convolution neural network, which comprises the following steps: (1) preprocessing an input image, converting the input image into a gray-scale image, and normalizing the gray-scale value of the gray-scale image to be between [0, 1] or [1, 1] and reassembled to a uniform size; (2) inputting the image data obtained in thestep (1) into a 7-layer micro-convolution neural network, training the micro-convolution neural network, and generating prediction boxes of different scales for class prediction and regression targetposition; (3) training records the error on the training set and the test error on the verification set of each iteration; 4) judging whether that los on the successive 5 iterative verification setsis reduced, if so, returning to the step 2) if the loss is not reduced, terminating the training, saving the parameters of the 7-layer microconvolution neural network, and checking the feature extraction effect. The invention uses 7-layer convolution neural network structure instead of complex VGG (Deep Convolution Neural Network for Large Scale Image Recognition), which can be trained and testedon ordinary machines, does not need high performance computing equipment such as GPU (Graphics Processor) with super performance, nor does it need pre-trained network, it can be trained and tested from scratch.

Description

technical field [0001] The invention belongs to the field of multimedia signal processing, and in particular relates to a real-time vehicle detection method based on a micro-convolution network. Background technique [0002] With the development of cities, the explosive growth of vehicles, and the complexity and change of roads, we have begun to pay more and more attention to intelligent transportation. The development of unmanned driving technology promotes the in-depth research of target detection, and the application of unmanned driving technology Real-time performance of target detection is more needed. [0003] Object detection is also widely used in many other fields of life. It extracts objects that people are interested in or pays attention to in the image, identifies the category of the object and determines the location of the target. This is a computer vision task and a meaningful research direction in the field of computer vision. With the development of the In...

AI Technical Summary

Problems solved by technology

[0005] Deep learning technology has developed rapidly in recent years, and more and more traditional algorithms have been surpassed by deep learning in terms of accuracy and precision. Object detection is a major branch of deep learning in the field of computer vision, and there are more and more More and more target detection algorithms have been proposed and studied by everyone, but there are very few algorithms that are actually implemented, because the deep network structure used in the research is too complex, it is difficult to converge during training, and a high-performance graphics processing unit (GPU) is required for real use. Supported, it cannot meet the real-time requirements on ordinary machines

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