A Real-time Detection Method of Pedestrians and Vehicles Based on Lightweight Deep Network

Abstract

The present invention provides a real-time detection method for pedestrians and vehicles based on a lightweight deep network. The lightweight deep network uses 5 modules + 3 kinds of convolution operations, and the meta-module only contains 2 kinds of convolution operations to realize the feature extraction function. . The jump connection mode between different modules and the more robust feature spectrum fusion technology of the present invention enable the network to achieve a better detection effect on pedestrians and vehicles when the model parameters are small, and can effectively detect images or Pedestrian vehicles in the video. The new deep network proposed by the present invention has the advantages of small model parameters, small computational complexity and high detection accuracy, and can realize real-time detection of pedestrians and vehicles on the embedded platform, and has good practicability and real-time performance .

Description

technical field [0001] The invention relates to deep learning technology in image processing. Background technique [0002] With the continuous growth of urban economic level and population, the number of vehicles and pedestrians on traffic roads also increases accordingly. [0003] The ensuing series of traffic problems such as road traffic congestion and frequent traffic accidents have put forward higher requirements for urban traffic construction. Therefore, a more efficient and rapid real-time detection method for pedestrians and vehicles has broad application prospects and urgent market demand. [0004] The research on pedestrian and vehicle detection methods at home and abroad has been carried out for decades. During this period, many outstanding scholars have proposed many effective detection methods. However, as far as the ultimate goal of computer vision technology-equivalent to the recognition ability of human beings, there is still a certain gap in the detection...

AI Technical Summary

Problems solved by technology

[0007] (3) The real background of pedestrians and vehicles is complicated;

[0010] (6) The occlusion situation of pedestrians and vehicles is complex and diverse;

However, these two methods have certain limitations in practical applications. The accuracy of the former is not high in practical applications. The ability to extract features can improve the detection accuracy of the deep network, but at the same time, the model parameters and computational complexity of the network are also greatly increased, which undoubtedly puts forward certain requirements for the memory and computing power of the hardware platform. The complexity is too large, especially on some embedded platforms with limited memory size and computing power, it is difficult to achieve real-time effects

[0015] VGG-16 is an existing classification network with good effect. It is often used as the feature extraction part of the target detection network. style platform

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