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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
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    References listed on IDEAS

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    1. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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