Illegal parking detection based on improved YOLOv5 model and ray method

Illegally parked vehicles reduce road traffic efficiency, and cause traffic congestion even traffic accidents. Traditional vehicle detection methods are perplexed by a large number of parameters and low accuracy. Here, we propose a method using the improved YOLOv5 model and ray method to detect ille...

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Bibliographic Details
Published inNanjing Xinxi Gongcheng Daxue Xuebao Vol. 16; no. 3; pp. 341 - 351
Main Authors Zhuang, Jianjun, Xu, Ziheng, Zhang, Ruoyu
Format Journal Article
LanguageChinese
Published Nanjing Nanjing University of Information Science & Technology 01.06.2024
Subjects
Accuracy
Feature extraction
Frames per second
Object recognition
Parameters
Real time
Traffic
Traffic accidents
Traffic accidents & safety
Traffic congestion
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Summary:Illegally parked vehicles reduce road traffic efficiency, and cause traffic congestion even traffic accidents. Traditional vehicle detection methods are perplexed by a large number of parameters and low accuracy. Here, we propose a method using the improved YOLOv5 model and ray method to detect illegally parked vehicles. First, a lightweight feature extraction module is designed to reduce the amount of model parameters. Second, the attention mechanism is added to the model to enhance its feature extraction ability from both channel dimension and spatial dimension to ensure the model's accuracy. Then, the mixed data is used to enhance and enrich the dataset samples thus improve the detection performance in complex backgrounds, and EIoU is selected as the loss function to improve the model's positioning performance.Experiments show that the mean accuracy of the improved YOLOv5 model reaches 91.35%, which is 1.01 percentage points higher than that of the original YOLOv5s,and the number of parameters is reduced b
ISSN:1674-7070
DOI:10.13878/j.cnki.jnuist.20230402001