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Traffic sign detection method based on improved YOLO v3 algorithm

A traffic sign and algorithm technology, applied in the field of computer vision technology and intelligent transportation, can solve the problems of low accuracy, slow speed, and difficulty in applying small-size traffic sign recognition, and achieve the effect of improving detection accuracy and increasing semantic information.

Active Publication Date: 2020-06-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0005] The purpose of the present invention is to invent a traffic sign detection algorithm based on the improved YOLO v3 algorithm for the existing traffic sign image recognition technology with low precision and slow speed, especially difficult to apply to small-sized traffic sign recognition. The algorithm greatly improves the accuracy and speed of traffic sign detection

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  • Traffic sign detection method based on improved YOLO v3 algorithm
  • Traffic sign detection method based on improved YOLO v3 algorithm
  • Traffic sign detection method based on improved YOLO v3 algorithm

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Embodiment Construction

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] like Figure 1-5 shown.

[0042] A traffic sign detection method based on the improved YOLO v3 algorithm. As an example, this embodiment uses the Chinese traffic sign dataset (Tsinghua-Tencent 100K, TT100K for short) produced by Tsinghua University, and selects the traffic sign categories that appear more than 100 times in this dataset as the research object. There are 6103 images in the training set. The set has 3067 images.

[0043] The implementation steps of the present invention include:

[0044] Step 1: Dataset preprocessing.

[0045] The traffic sign target is small and the distribution on the picture is relatively sparse. In order to reduce the imbalance of positive and negative samples, the original image of the training set is cropped. The cropping flowchart is attached. figure 1 , the specific cutting steps are as follows:

[0046]...

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Abstract

The invention discloses a traffic sign detection algorithm based on an improved YOLO v3 algorithm. According to the method, a feature extraction network capable of maintaining high-resolution representation is designed to replace DarkNet-53 in an original YOLO v3 algorithm, so that the detection precision of a small-size target traffic sign is improved, and the parameter quantity of the algorithmis reduced; attention of the detection algorithm to small and medium-sized targets is increased by fusing the feature maps participating in prediction; and optimizing the loss function by using a GIOUalgorithm and a local loss algorithm. According to the method, the detection accuracy of the small-size traffic sign is improved, and the traffic sign can be rapidly and accurately detected and identified on a complex traffic road.

Description

technical field [0001] The invention relates to the field of computer vision technology and intelligent transportation technology, in particular to a traffic sign image recognition method, in particular to a traffic sign detection method based on an improved YOLO v3 algorithm, which can be applied to advanced assisted driving technology traffic sign detection. Background technique [0002] In recent years, with the advancement of science and technology, in order to reduce the loss of life and property caused by traffic accidents, advanced assisted driving systems are becoming more and more perfect. As an important part of the advanced assisted driving system, the traffic sign detection technology mainly obtains the road condition information around the car through the on-board camera, detects and recognizes the traffic signs according to the captured video, and then transmits the recognition result to the driver or intelligent transportation. Other parts of the system to ac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/582G06N3/045G06F18/23213G06F18/241Y02T10/40
Inventor 陆开胜黎向锋王建明左敦稳张丽萍张立果叶磊唐浩刘安旭刘晋川王子旋
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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