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Cited by 123Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Fast and accurate object detector for autonomous driving based on improved YOLOv5

Open access
Jia, Xiang, Tong, Ying, Qiao, Hongming, Li, Man, Tong, Jiangang, Liang, Baoling·2023·Scientific Reports
DOI 10.1038/s41598-023-36868-wPMC PMC10272162
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Abstract

Autonomous driving is an important branch of artificial intelligence, and real-time and accurate object detection is key to ensuring the safe and stable operation of autonomous vehicles. To this end, this paper proposes a fast and accurate object detector for autonomous driving based on improved YOLOv5. First, the YOLOv5 algorithm is improved by using structural re-parameterization (Rep), enhancing the accuracy and speed of the model through training-inference decoupling. Additionally, the neural architecture search method is introduced to cut redundant branches in the multi-branch re-parameterization module during the training phase, which ameliorates the training efficiency and accuracy. Finally, a small object detection layer is added to the network and the coordinate attention mechanism is added to all detection layers to improve the recognition rate of the model for small vehicles and pedestrians. The experimental results show that the detection accuracy of the proposed method on the KITTI dataset reaches 96.1%, and the FPS reaches 202, which is superior to many current mainstream algorithms and effectively improves the accuracy and real-time performance of unmanned driving object detection.

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Crossref · 10.1038/s41598-023-36868-w

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Figures

Figure 1 Network architecture of YOLOv5.
Figure 1 Network architecture of YOLOv5.CC BY · PubMed Central
Figure 2 Structural reparameterization module.
Figure 2 Structural reparameterization module.CC BY · PubMed Central
Figure 3 RepNAS module.
Figure 3 RepNAS module.CC BY · PubMed Central
Figure 4 Diagrams of three attention mechanisms. (a) SE. (b) CBAM. (c) CA.
Figure 4 Diagrams of three attention mechanisms. (a) SE. (b) CBAM. (c) CA.CC BY · PubMed Central
Figure 5 Improved YOLOv5 algorithm.
Figure 5 Improved YOLOv5 algorithm.CC BY · PubMed Central
Figure 6 Detection result comparison (left: YOLOv5; right: improved YOLOv5).
Figure 6 Detection result comparison (left: YOLOv5; right: improved YOLOv5).CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 28

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Cited by

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Metadata

Authors
Jia, X., Tong, Y., Qiao, H., Li, M., Tong, J., & Liang, B.
Affiliations
  • China Telecom (China) (CN)
  • China Telecom (CN)
Corresponding author
Jia, Xiang
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2023-06-15
Volume
13
Issue
1
Pages
9711-9711
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-023-36868-w
PMC
PMC10272162
OpenAlex
W4380870677
Cited by
123
References
7
Citations by year
2026: 21 · 2025: 49 · 2024: 45 · 2023: 8
FWCI
11.16
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
object detection, autonomous driving, YOLOv5, reparameterization, neural architecture search, small object detection, coordinate attention, real-time detection, KITTI dataset
MeSH terms
Pedestrians, Autonomous Vehicles, Algorithms, Artificial Intelligence, Excipients, Humans

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