Real-time detection method of road vehicles based on deep learning ssd framework

Abstract

The invention discloses a real-time detection method for road vehicles based on a deep learning SSD framework, which mainly solves the problem of low detection accuracy in the prior art under conditions of crowded roads and complex weather conditions. The implementation plan is: 1. Take several videos of driving vehicles in the traffic arteries, and capture the window information of the vehicles in each frame of the video by manual labeling; 2. Use VGG‑16 in the classification network as the basic network , build the SSD300*300 detection framework, extract the features of some of the feature extraction layers and connect them as the recognition feature vector, and input them into the loss function; 3. Use the training samples for training, and substitute the trained detection model into the detection framework Middle; 4. Set the correlation threshold and use the trained detection model to detect the vehicles in the test video. The invention not only greatly improves the detection accuracy, but also achieves the effect of real-time detection, and can be used for vehicle detection in complex scenes.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and mainly relates to a real-time detection method for road vehicles, which can be used for detection of running vehicles. Background technique [0002] With the continuous improvement of living standards in modern society, as an important means of transportation, the number of automobiles shows a trend of rapid growth, which brings great challenges to traffic supervision. Although the rapid increase in the number of cars has brought a lot of convenience to people's lives, it has also brought a series of traffic problems such as running red lights, traffic jams, speeding, and traffic accidents. The traditional road traffic management methods relying on manpower or basic transportation facilities can no longer meet the current development needs. In recent years, technologies such as computer technology, artificial intelligence and pattern recognition have been vigorously developed and wi...

AI Technical Summary

Problems solved by technology

The traditional road traffic management methods relying on manpower or basic transportation facilities can no longer meet the current development needs. In recent years, technologies such as computer technology, artificial intelligence and pattern recognition have been vigorously developed and widely used, and these technologies have also penetrated into traffic services. Industry, in this context, traffic monitoring technology based on deep learning convolutional neural network came into being

[0003] At present, in the research on car model recognition based on deep convolutional neural network, "Car Model Recognition Based on Deep Convolutional Neural Network" by Deng Liu of Southwest Jiaotong University, the research mainly distinguishes vehicle types such as cars, trucks and buses, but this The research is based on the small block of vehicle image as input, the classification and recognition of the combination of simple convolutional neural network and SVM, and does not include the research on the recognition of vehicle types in harsh environments such as rainy days, foggy days, snowy days and congestion.

This kind of small image block as input cannot combine background information well, and capturing the entire vehicle information makes the deep network capture a lot of redundant information that has nothing to do with classification and recognition, and the classification and recognition network cannot achieve end-to-end detection , not only the detection accuracy is not high, but also causes a waste of detection time, which cannot meet the requirements of real-time detection

Images