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
CN109447034BActive Publication Date: 2021-04-06BEIJING INFORMATION SCI & TECH UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING INFORMATION SCI & TECH UNIV
Publication Date
2021-04-06

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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.
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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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