SMA-YOLO:在边缘设备上实时检测车辆的新方法
Haixia Liu1, Yingkun Song2, Yongxing Lin1,2
1Keyi College, Zhejiang Sci-Tech University, Shaoxing 312369, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究介绍了SMA-YOLO,这是智能交通系统的高效车辆检测模型. 它显著提高了速度和精度,同时减少了尺寸,使其成为边缘设备的理想选择.
科学领域:
- 计算机视觉
- 人工智能
- 机器学习
背景情况:
- 智能交通管理和自动驾驶在很大程度上依赖于准确的车辆检测.
- 目前的深度学习模型在复杂环境中难以实现速度,计算成本和准确性, 阻碍了边缘部署.
研究的目的:
- 开发一个轻量级和高效的车辆检测模型,用于边缘设备的实时应用.
- 提高现有的YOLOv7模型的检测速度和准确性.
主要方法:
- 拟议的SMA-YOLO,将MobileNetV3骨干,SimAM注意力,ACON激活和SIoU损失与YOLOv7集成在一起.
- 实施模型修改以减少计算负载和增强特征提取.
主要成果:
- SMA-YOLO在模型大小,计算要求和参数方面实现了显著的减少.
- 在UA-DETRAC数据集上证明了提高检测速度和保持高准确性.
结论:
- SMA-YOLO提供了一个可行的解决方案,用于在嵌入式系统上部署高效的车辆检测.
- 拟议的模型平衡了实时智能交通系统的性能和资源效率.
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