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<p>To achieve real-time detection on resource-constrained edge devices, a lightweight vehicle detection algorithm named YOLO-edge was developed based on the YOLOv5s framework. In YOLO-edge, a slim neck was designed to reduce the computational cost in the feature channel fusion process. A modified fast spatial pyramid pooling technique enhances feature extraction efficiency. A multi-scale feature fusion architecture with a broad receptive field integrates shallow and deep features, preserving details for comprehensive feature representation. The loss function was optimized to improve the accuracy of bounding box predictions. Experimental results demonstrate that YOLO-edge achieves a 5.1% increase in mean average precision (mAP) compared to YOLOv5s. Real-time detection rates of 34 and 47 frames per second (FPS) are achieved on edge devices Jetson TX2 and Jetson Orin NX, respectively. YOLO-edge exhibited superior speed and accuracy on edge devices, outperforming state-of-the-art YOLO methods in vehicle detection.</p>

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YOLO-edge: real-time vehicle detection for edge devices

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Xuewen Li,
  • Liangyan Chen,
  • Tong Huang,
  • An Yang,
  • Weihua Liu

摘要

To achieve real-time detection on resource-constrained edge devices, a lightweight vehicle detection algorithm named YOLO-edge was developed based on the YOLOv5s framework. In YOLO-edge, a slim neck was designed to reduce the computational cost in the feature channel fusion process. A modified fast spatial pyramid pooling technique enhances feature extraction efficiency. A multi-scale feature fusion architecture with a broad receptive field integrates shallow and deep features, preserving details for comprehensive feature representation. The loss function was optimized to improve the accuracy of bounding box predictions. Experimental results demonstrate that YOLO-edge achieves a 5.1% increase in mean average precision (mAP) compared to YOLOv5s. Real-time detection rates of 34 and 47 frames per second (FPS) are achieved on edge devices Jetson TX2 and Jetson Orin NX, respectively. YOLO-edge exhibited superior speed and accuracy on edge devices, outperforming state-of-the-art YOLO methods in vehicle detection.