Traffic target detection method and system based on improved YOLOv4

A target detection and detection method technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as difficulty in model deployment, calculation speed, difficulty in obtaining anchor frame configuration, large amount of model parameters, etc., to reduce the complexity of the model Accuracy, high accuracy, and high detection accuracy
CN114495029APending Publication Date: 2022-05-13CHINA UNIV OF MINING & TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
CHINA UNIV OF MINING & TECH
Publication Date
2022-05-13

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Abstract

The invention discloses a traffic target detection method and system based on improved YOLOv4, the detection system comprises a MobileViT-S backbone network, an SPP feature pyramid network, a PANet feature enhancement network and a target detection head, and convolution used in the PANet feature enhancement network and the target detection head is deep separable convolution. The method can be used for detecting pedestrians, vehicles and traffic light targets in an intelligent traffic scene, high detection accuracy is achieved on the basis of light weight, the omission ratio is low, and the detection effect is good.
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Description

technical field

[0001] The present invention relates to the technical field of computer vision, in particular to the technical field of target detection based on the YOLOv4 algorithm. Background technique

[0002] With the rapid development of computer vision, object detection is gradually applied to people's daily life, which brings great convenience. Among them, in order to solve various traffic problems under complex traffic conditions, the detection of traffic objects has become a research focus in the field of computer vision.

[0003] The targets in the traffic background have the characteristics of large number, severe occlusion, and mostly small targets. In the current background, target detection often has the disadvantages of serious missed detection, slow detection rate, and difficult deployment. Therefore, it is particularly important to optimize the target detection model to make it lightweight for easy deployment, speed up detection, and make it more suitable ...

Claims

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