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Traffic sign detection method in automatic driving based on yolov3 network

A traffic sign and automatic driving technology, which is applied in the field of traffic sign detection, can solve problems such as low detection accuracy and detection speed that cannot meet real-time requirements, and achieve the effects of enhancing robustness, improving detection accuracy, and satisfying real-time performance

Active Publication Date: 2021-04-06
BEIJING INFORMATION SCI & TECH UNIV
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  • Description
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing YOLOv3 network target detection algorithm has low detection accuracy and the detection speed cannot meet the real-time requirements

Method used

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  • Traffic sign detection method in automatic driving based on yolov3 network
  • Traffic sign detection method in automatic driving based on yolov3 network
  • Traffic sign detection method in automatic driving based on yolov3 network

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specific Embodiment approach 1

[0022] Specific embodiment one: the traffic sign detection method in the automatic driving based on YOLOv3 network described in this embodiment, this method specifically comprises the following steps:

[0023] Step 1, based on the GTSDB data set, make training set data and test set data with traffic sign target labels;

[0024] Step 2. Cluster the real target frames marked in the training set data, and use the area intersection over union ratio (IOU) as the rating index to obtain the initial candidate target frame of the predicted traffic sign target in the training set data, and use the initial candidate target frame as The initial network parameters of the YOLOv3 network; (the advantage of this is that the convergence speed of the training process can be accelerated); call the initial network parameters of the YOLOv3 network, and input the training set data into the YOLOv3 network for training until the loss function of the training set data output Values ​​less than or equa...

specific Embodiment approach 2

[0028] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: the specific process of the step one is:

[0029] The GTSDB data set contains M images in total. After marking the traffic signs in the M images, the marked M images are randomly divided into two parts: a training set and a test set.

specific Embodiment approach 3

[0030] Embodiment 3: This embodiment is different from Embodiment 2 in that: the data volume ratio of the training set and the test set is 8:1.

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Abstract

A traffic sign detection method in automatic driving based on the YOLOv3 network, which belongs to the field of traffic sign detection. The invention solves the problems that the existing YOLOv3 network target detection algorithm has low detection accuracy and the detection speed cannot meet the real-time requirement. The invention proposes an improved loss function, thereby reducing the influence of large target errors on the detection effect of small targets, and improving the detection accuracy of small-sized targets; an improved activation function is proposed, which retains negative values ​​and reduces propagation to the next layer. Changes and information enhance the robustness of the algorithm to noise; through the K-means algorithm, the real borders in the traffic sign dataset are clustered to realize the prefetching of the target border position and accelerate the convergence of the network. The detection accuracy mAP of the traffic sign detection model of the present invention on the test set reaches 92.88%, and the detection speed reaches 35FPS, which fully meets the real-time requirement. The invention can be applied in the field of traffic sign detection.

Description

technical field [0001] The invention belongs to the field of traffic sign detection, and in particular relates to a traffic sign detection method in automatic driving. Background technique [0002] Object detection is an important research direction in the field of autonomous driving. Its main detection targets are divided into two categories: stationary targets and moving targets. Stationary targets such as traffic lights, traffic signs, lanes, obstacles, etc.; moving targets such as vehicles, pedestrians, non-motor vehicles, etc. Among them, traffic sign detection provides rich and necessary navigation information for unmanned vehicles during driving, which is a fundamental work of great significance. [0003] Traditional target detection methods are mainly divided into the following steps: preprocessing, selecting candidate regions, extracting target features and feature classification. Commonly used features such as SIFT (scale-invariant feature transform), HOG (histo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/582G06V2201/07G06F18/23213G06F18/214
Inventor 王超
Owner BEIJING INFORMATION SCI & TECH UNIV
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