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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

Pending Publication Date: 2022-05-13
CHINA UNIV OF MINING & TECH
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Problems solved by technology

[0006] For the defects of the prior art, the object of the present invention is to propose a traffic target detection method and system based on improved YOLOv4, which can solve the following problems that YOLOv4 can produce in the single-stage target detection algorithm in the prior art: (1 ) small targets are seriously missed; (2) the model is complex, the model parameters are large, the model is difficult to deploy to mobile embedded devices, and the calculation speed is slow; (3) the K-Means clustering used is random, and it is difficult to obtain the best Anchor box configuration for

Method used

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  • Traffic target detection method and system based on improved YOLOv4
  • Traffic target detection method and system based on improved YOLOv4

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Embodiment 1

[0141] Carry out traffic target detection through the detection system described in the above specific implementation mode, set the initial learning rate to 0.001, the learning rate attenuation coefficient to 0.96, the number of training rounds to 200 rounds, and the batchsize to 8. The obtained detection results are as attached Figure 8 As shown, it can be seen that the system can accurately detect targets of different sizes.

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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.

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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Application Information

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IPC IPC(8): G06V20/54G06V10/762G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/23213Y02T10/40
Inventor 袁小平王准赵耀倪梓昂李元博孙乐义
Owner CHINA UNIV OF MINING & TECH
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