IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2023i1p124-d1310411.html
   My bibliography  Save this article

Light “You Only Look Once”: An Improved Lightweight Vehicle-Detection Model for Intelligent Vehicles under Dark Conditions

Author

Listed:
  • Tianrui Yin

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Wei Chen

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Bo Liu

    (Wuhan Zhongyuan Electronics Group Co., Ltd., Wuhan 430010, China)

  • Changzhen Li

    (School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China)

  • Luyao Du

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Vehicle detection is crucial for traffic surveillance and assisted driving. To overcome the loss of efficiency, accuracy, and stability in low-light conditions, we propose a lightweight “You Only Look Once” (YOLO) detection model. A polarized self-attention-enhanced aggregation feature pyramid network is used to improve feature extraction and fusion in low-light scenarios, and enhanced “Swift” spatial pyramid pooling is used to reduce model parameters and enhance real-time nighttime detection. To address imbalanced low-light samples, we integrate an anchor mechanism with a focal loss to improve network stability and accuracy. Ablation experiments show the superior accuracy and real-time performance of our Light-YOLO model. Compared with EfficientNetv2-YOLOv5, Light-YOLO boosts mAP@0.5 and mAP@0.5:0.95 by 4.03 and 2.36%, respectively, cuts parameters by 44.37%, and increases recognition speed by 20.42%. Light-YOLO competes effectively with advanced lightweight networks and offers a solution for efficient nighttime vehicle-detection.

Suggested Citation

  • Tianrui Yin & Wei Chen & Bo Liu & Changzhen Li & Luyao Du, 2023. "Light “You Only Look Once”: An Improved Lightweight Vehicle-Detection Model for Intelligent Vehicles under Dark Conditions," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:124-:d:1310411
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/1/124/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/1/124/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luca Zanni, 2006. "An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines," Computational Management Science, Springer, vol. 3(2), pages 131-145, April.
    2. Peng Han & Xinyue Yang & Yifei Zhao & Xiangmin Guan & Shengjie Wang, 2022. "Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network," Sustainability, MDPI, vol. 14(9), pages 1-13, May.
    3. Andrej Čopar & Blaž Zupan & Marinka Zitnik, 2019. "Fast optimization of non-negative matrix tri-factorization," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    4. Hoi-Ming Chi & Okan K. Ersoy & Herbert Moskowitz & Kemal Altinkemer, 2007. "Toward Automated Intelligent Manufacturing Systems (AIMS)," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 302-312, May.
    5. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
    6. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    7. Luminita STATE & Catalina COCIANU & Cristian USCATU & Marinela MIRCEA, 2013. "Extensions of the SVM Method to the Non-Linearly Separable Data," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 173-182.
    8. C. J. Lin & S. Lucidi & L. Palagi & A. Risi & M. Sciandrone, 2009. "Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 107-126, April.
    9. Andrea Manno & Laura Palagi & Simone Sagratella, 2014. "A Class of Convergent Parallel Algorithms for SVMs Training," DIAG Technical Reports 2014-17, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    10. Giampaolo Liuzzi & Laura Palagi & Mauro Piacentini, 2010. "On the convergence of a Jacobi-type algorithm for Singly Linearly-Constrained Problems Subject to simple Bounds," DIS Technical Reports 2010-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    11. Farah Mohammad & Saad Al Ahmadi, 2023. "Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    12. Wang, Yongqiang & Huang, Donghua & Sun, Kexin & Shen, Hongzheng & Xing, Xuguang & Liu, Xiao & Ma, Xiaoyi, 2023. "Multiobjective optimization of regional irrigation and nitrogen schedules by using the CERES-Maize model with crop parameters determined from the remotely sensed leaf area index," Agricultural Water Management, Elsevier, vol. 286(C).
    13. Luminita STATE & Catalina COCIANU & Doina FUSARU, 2010. "A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(3), pages 128-139.
    14. Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Daniel Horn & Aydın Demircioğlu & Bernd Bischl & Tobias Glasmachers & Claus Weihs, 2018. "A comparative study on large scale kernelized support vector machines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 867-883, December.
    16. Weiwei Ding & Yuhong Zhang & Liya Huang, 2022. "Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks," IJERPH, MDPI, vol. 19(6), pages 1-14, March.
    17. Heguang Sun & Lin Zhou & Meiyan Shu & Jie Zhang & Ziheng Feng & Haikuan Feng & Xiaoyu Song & Jibo Yue & Wei Guo, 2024. "Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation," Agriculture, MDPI, vol. 14(3), pages 1-18, March.
    18. Sachin Kumar & Aditya Sharma & B Kartheek Reddy & Shreyas Sachan & Vaibhav Jain & Jagvinder Singh, 2022. "An intelligent model based on integrated inverse document frequency and multinomial Naive Bayes for current affairs news categorisation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1341-1355, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:124-:d:1310411. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.