Method for detecting and identifying traffic signal lamps in real time

A technology for traffic lights and real-time detection, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of insensitivity to small target detection, low detection accuracy of traffic lights, etc., and achieve the effect of high-precision real-time detection

Active Publication Date: 2020-12-18
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0008] In order to solve the problem that the YOLOv4 algorithm used in the existing traffic signal light detection is not sensitive to small target detectio...

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  • Method for detecting and identifying traffic signal lamps in real time
  • Method for detecting and identifying traffic signal lamps in real time
  • Method for detecting and identifying traffic signal lamps in real time

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Embodiment Construction

[0052] In conjunction with the accompanying drawings, the realization of a method for real-time detection and identification of traffic lights according to the present invention is described as follows:

[0053] 1. YOLOv4 algorithm

[0054] 1.1 YOLOv4 algorithm principle

[0055] The YOLOv4 algorithm divides the network input into S×S grid units, and each grid unit predicts B bounding boxes, bounding box confidence and C category probabilities. The confidence of the predicted bounding box reflects whether the predicted bounding box contains the object, and the accuracy of the position when the object is included. The accuracy is expressed as the intersection over union (IOU) of the predicted bounding box and the real bounding box. The calculation formula for:

[0056]

[0057] In the formula: confidence is the confidence of the bounding box, and Pr(object) is the probability that there is an object to be detected in the grid.

[0058] By setting the category confidence t...

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Abstract

The invention discloses a method for detecting and identifying traffic signal lamps in real time, and belongs to the field of traffic light detection and identification. The method is provided for solving the problem that the YOLOv4 algorithm is insensitive to small target detection, so that the traffic signal lamp detection precision is low. A shallow feature enhancement mechanism is provided, two shallow features in different stages in a feature extraction network are fused with high-level semantic features obtained after two times of up-sampling respectively, so that the scale of two detection layers is increased, and the positioning and color determining capability of the network for small targets are improved. A bounding box uncertainty prediction mechanism is introduced, output coordinates of a prediction bounding box are modeled, a Gaussian model is added to calculate the uncertainty of coordinate information, and the reliability of the prediction bounding box is improved. A LISA traffic signal lamp data set is used for carrying out detection and identification experiments, and the AUC value of an improved YOLOv4 algorithm in the detection experiments is 97.58% and is increased by 7.09% compared with VVA. The mean value of the average precision of the improved YOLOv4 algorithm in the recognition experiment is 82.15%, which is 2.86% higher than that of the original YOLOv4algorithm. The improved YOLOv4 algorithm improves the detection and identification precision of the traffic signal lamp.

Description

technical field [0001] The invention relates to a real-time detection and recognition method for traffic signal lamps, belonging to the field of detection and recognition of traffic signal lamps. Background technique [0002] Traffic signal detection is one of the important branches in the field of intelligent driving, and its recognition accuracy directly affects the safety of intelligent driving. Traffic signal detection systems often use industrial cameras to collect road condition information, and the traffic signal images collected in actual traffic scenes have complex backgrounds. The signal lights in the image occupy less pixels, and the feature structure is sparse, which increases the difficulty of algorithm recognition. Therefore, it is of great significance to study more effective small target detection algorithms for the traffic light detection problem. [1-2] . [0003] Traffic signal detection has experienced several years of development, and there are many exc...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/00G06V10/56G06V2201/07G06N3/048G06N3/045G06F18/23213G06F18/253
Inventor 王庆岩周长越王玉静康守强谢金宝康成璐
Owner HARBIN UNIV OF SCI & TECH
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