Vehicle target detection method

A target detection and vehicle technology, applied in the field of vehicle target detection, can solve the problems of inaccurate detection, complex operation process, large amount of calculation, etc., and achieve the effect of comprehensive detection results, simplified operation process and small amount of calculation.

Inactive Publication Date: 2018-05-08
HENAN UNIVERSITY OF TECHNOLOGY +1
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AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a vehicle target detection method to solve the technical problems of traditional vehicle detection methods such as large calculation, complex operation process and inaccurate detection

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Embodiment 1

[0031] Embodiment 1: a kind of vehicle object detection method, see figure 1 , firstly select suitable training samples from the ImageNet dataset, extract image features and mark the vehicle targets in the samples. Then input the samples into the RPN network for training until the network converges, then input the convolutional layer network parameters obtained by the RPN network training into the improved Faster R-CNN network for training until the network converges, and finally input the parameters of each network layer into the model for further Detection and recognition of vehicle objects.

[0032] When making the training samples in this embodiment, the format of the VOC 2007 data set and evaluation algorithm tools are used to convert the ImageNet data set into the format of the VOC 2007 data set. The specific steps are as follows: (1) Calculate the width, Height, aspect ratio and target width, height, and aspect ratio; (2) Screen out the required pictures from the Image...

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Abstract

The invention discloses a vehicle target detection method and aims to solve the technical problems that a traditional method is complicated in detection process, large in calculated quantity and inaccurate in detection. According to the method, a VOC 2007 vehicle dataset is made according to an ImageNet dataset; a training model is configured by the adoption of a caffe deep learning framework andan improved Faster R-CNN algorithm, and an Inception network structure is introduced to perform feature extraction on an image; a sliding window with the area of 642 is additionally arranged and usedfor detecting a small target; a target detection problem in a scene is converted into a target dichotomy problem, and a vehicle target is detected and recognized; an RPN loss function is used to perform optimization; and a SoftMax algorithm is utilized to classify vehicle image features, and therefore a final result is obtained. The method has the advantages that the calculated quantity of the image features is reduced, the detection process is simplified, the image feature extraction capability is enhanced, network recognition precision is improved, and the detection result is more comprehensive.

Description

technical field [0001] The invention relates to the technical field of target information detection, in particular to a vehicle target detection method. Background technique [0002] In recent years, with the rapid growth of the number of traffic vehicles, traffic supervision is facing great challenges. Vehicle object detection, as a key technology for constructing video surveillance of traffic conditions, has long been widely concerned by researchers at home and abroad. Object detection is an important branch of image processing and computer vision, and its research methods are mainly divided into methods based on background modeling and methods based on appearance feature information. [0003] Scholars at home and abroad have made many attempts using traditional machine learning methods. By extracting target features, such as HOG (histogram of oriented gradient), SIFT (scale invariant feature transform) and other methods, and inputting the extracted features into the clas...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/584G06N3/045G06F18/24
Inventor 张庆辉万晨霞韩伟良
Owner HENAN UNIVERSITY OF TECHNOLOGY
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