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Remote traffic sign detection and recognition method based on F-RCNN

A traffic sign and recognition method technology, applied in the field of intelligent transportation, can solve the problem of low precision

Active Publication Date: 2019-08-23
NORTHEAST GASOLINEEUM UNIV
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0007] The purpose of the present invention is to provide a long-distance traffic sign detection and recognition method based on F-RCNN, which is used to solve the problem of low accuracy of the existing short-distance detection and recognition method when performing long-distance traffic sign detection and recognition

Method used

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  • Remote traffic sign detection and recognition method based on F-RCNN

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Embodiment

[0164] The present invention adopts Tsinghua-Tencent 100K as the data set of model training study, to 44 kinds of common traffic signs (refer to image 3 ) were detected and identified. In this data set, the resolution of each traffic scene is 2048×2048, and the size of traffic signs is between 0-32 pixels and 32-96 pixels, accounting for 41.6% and 49.1% of the data set, or 90.7% of the traffic signs The proportion of the size of the traffic scene is less than 1%, which belongs to the long-distance traffic sign detection and recognition situation.

[0165] F-RCNN model training and on-the-spot test situation description (test is in order to grasp the feasibility of the present invention's 1st step~8th step)

[0166] In the process of training the F-RCNN model, in order to eliminate the imbalance of the sample set, for categories with less than 100 pictures, the method of resampling is used in each training iteration to make the number of pictures exceed 1000. The ratio of tr...

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Abstract

The invention relates to a remote traffic sign detection and recognition method based on F-RCNN, which comprises: 1, preprocessing the traffic sign image sample set; 2, pre-training VGG-16 in F-RCNN;3, inputting the traffic sign training data set to the VGG-16 to complete feature extraction; 4, constructing a fusion feature map; enabling the region generation network RPN in the F-RCNN to performregion generation according to the fusion feature map to obtain a candidate region of the traffic sign; 6, inputting all candidate regions to the RoI-Pooling layer in the F-RCNN to generate a fixed-size feature vector; 7, sending the feature vector to the extreme learning machine network to output the category and location of the traffic sign; 8, training the F-RCNN model by the contribution adaptive loss function; 9, completing the traffic sign detection and identification of the actual scene. The invention realizes the detection and identification of the long-distance traffic sign, and the recognition precision is high.

Description

[0001] 1. Technical field: [0002] The invention relates to the field of intelligent transportation oriented to unmanned driving and assisted driving, and solves the long-distance detection and recognition method of road traffic signs, and specifically relates to the long-distance traffic sign detection and recognition method based on F-RCNN. [0003] 2. Background technology: [0004] In the field of intelligent transportation, traffic sign detection and recognition is an important research issue for systems such as unmanned driving and assisted driving. A lot of research work has been done on this at home and abroad, but there are still great deficiencies, which cannot be practically applied to practice. The reasons are as follows: (1) Some detection and recognition methods use the public German traffic sign datasets GTSRB and GTSDB. Traffic signs occupy a large proportion of the image, and the detection distance of traffic signs is short, which is difficult to adapt to the ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04
CPCG06V20/582G06V10/40G06N3/045G06F18/253
Inventor 杜娟刘志刚刘贤梅王辉刘苗苗王梅
Owner NORTHEAST GASOLINEEUM UNIV
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