一个基于OpenMV视觉模块的轻量级混合检测系统,用于嵌入式运输车辆
Xinxin Wang1, Hongfei Gao1, Xiaokai Ma1
1Department of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450000, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究引入了一种轻量化混合系统,用于实时检测实验室物品运输中的无人驾驶车辆. 该系统结合了HSV颜色细分和FOMO MobileNetV2深度学习模型,以高效准确地识别目标.
科学领域:
- 机器人和自动化 机器人和自动化
- 计算机视觉 计算机视觉
- 嵌入式系统 嵌入式系统
背景情况:
- 实验室物品运输的智能控制系统需要实时对象检测.
- 移动嵌入式无人驾驶汽车需要轻量级和高效的检测解决方案.
研究的目的:
- 开发一种轻量化混合检测系统,用于实验室物品运输中的实时物体检测.
- 提高移动嵌入式无人驾驶汽车对象检测的准确性和效率.
主要方法:
- 两个阶段的检测机制:HSV颜色细分用于远距离 (>32厘米) 和FOMO MobileNetV2深度学习用于近距离 (≤32厘米) 检测.
- 将MobileNetV2骨干与FOMO对象检测算法集成在一起.
- 硬件集成:OpenMV H7 Plus摄像头,OLED显示屏,蓝牙,超声波传感器和伺服板倾斜.
主要成果:
- 在自建数据集上实现了94.1%的平均分类准确度,比基线MobileNetV2.5%的改进.
- 在嵌入式终端上,实时处理性能为55fps.
- 实验室检测目标的有效实时识别和跟踪.
结论:
- 拟议的轻量化混合检测系统为实验室中小型物品的自动化,低功耗运输提供了有效和实用的解决方案.
- 该系统在实时对象检测和跟踪中表现出强大的性能,用于智能控制应用.
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