Improved YOLOv5 network for real-time multi-scale traffic sign detection

Traffic sign detection is a challenging task for the unmanned driving system, especially for the detection of multi-scale targets and the real-time problem of detection. In the traffic sign detection process, the scale of the targets changes greatly, which will have a certain impact on the detection...

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Vydané v:Neural computing & applications Ročník 35; číslo 10; s. 7853 - 7865
Hlavní autori: Wang, Junfan, Chen, Yi, Dong, Zhekang, Gao, Mingyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.04.2023
Springer Nature B.V
Predmet:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data augmentation
Data Mining and Knowledge Discovery
Datasets
Feature extraction
Feature maps
Image Processing and Computer Vision
Modules
Original Article
Probability and Statistics in Computer Science
Real time
Target detection
Traffic control
Traffic signs
Data augmentation
YOLOv5
Multi-scale target
AF-FPN
ISSN:0941-0643, 1433-3058
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Shrnutí:Traffic sign detection is a challenging task for the unmanned driving system, especially for the detection of multi-scale targets and the real-time problem of detection. In the traffic sign detection process, the scale of the targets changes greatly, which will have a certain impact on the detection accuracy. Feature pyramid is widely used to solve this problem, but due to the diversity of traffic sign sizes, it cannot accurately extract multi-size feature maps, thus destroying the feature consistency between traffic signs. Moreover, in practical application, it is difficult for common methods to improve the detection accuracy of multi-scale traffic signs while ensuring real-time detection. In this paper, we propose an improved feature pyramid model, named AF-FPN, which utilizes the adaptive attention module (AAM) and feature enhancement module (FEM) to reduce the information loss in the process of feature map generation and enhance the representation ability of the feature pyramid. We replaced the original feature pyramid network in YOLOv5 with AF-FPN, which improves the detection performance for multi-scale targets of the YOLOv5 network under the premise of ensuring real-time detection. Furthermore, a new automatic learning data augmentation method is proposed to enrich the dataset and improve the robustness of the model to make it more suitable for practical scenarios. Extensive experimental results on the Tsinghua-Tencent 100 K (TT100K) dataset demonstrate that compared with several state-of-the-art methods, our method is more universal and superior.
Bibliografia:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-08077-5