A vehicle detection method based on deep learning

Abstract

The invention is suitable for the field of vehicle detection, and provides a vehicle detection method based on deep learning, and the method comprises the following steps: S1, obtaining a plurality ofimages containing vehicles through a video stream, carrying out the manual marking of the images, and carrying out the dividing of the images into a training set and a verification set of a detectionmodel according to a proportion; S2, constructing a target detection PDN network based on the DDN; S3, performing model training on the target detection PDN network by using a training set, and selecting a model with an optimal performance of a verification set; And S4, on the basis of the optimal model, performing prediction on the GPU server to realize target detection on the video stream. Thetechnical problems that in the prior art, in actual target detection, a video is usually processed, an algorithm is needed to carry out target detection in real time, a small part of researchers' methods meet the requirements on speed, however, with the cost of sacrificing precision, the precision is low, an application cannot fall to the ground, and a large amount of missed detection and false detection cannot be accepted are solved.

Description

technical field [0001] The invention belongs to the field of vehicle detection, in particular to a vehicle detection method based on deep learning. Background technique [0002] Target detection is an important topic in the field of computer vision. The main task is to locate the target of interest from the image. It is necessary to accurately judge the specific category of each target and give the bounding box of each target. Due to the deformation of the target due to factors such as viewing angle, occlusion, and pose, target detection becomes a challenging task. [0003] Traditional object detection methods are mainly divided into six steps: preprocessing, window sliding, feature extraction, feature selection, feature classification and postprocessing. Traditional object detection is generally by designing some better artificial features, and then using a classifier for classification. As the target detection accuracy and speed requirements are getting higher and higher...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a vehicle detection method based on deep learning, which aims to solve the problem of a small number of researchers in the actual target detection in the prior art, which usually processes the video and requires the algorithm to be able to detect the target in real time. The method meets the requirements in terms of speed, but at the cost of sacrificing accuracy, the low accuracy makes the application impossible to implement, and a large number of missed and false detections are unacceptable technical problems

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