基于改进的YOLOv2优化智能引导车辆视觉导航
Lei Hua1, Xing Wu2, Jinwang Gu1
1Jiangsu University of Science and Technology-Zhangjiagang Campus, Zhenjiang, Jiangsu 215600, China.
The Review of scientific instruments
|June 18, 2024
概括
本研究介绍了用于智能引导车辆 (IGV) 视觉导航的简化YOLOv2模型,提高了复杂端口条件下的实时检测精度. 增强的方法提高了要求IGV硬件和可变天气场景的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 由于传统方法的局限性,智能引导车辆 (IGV) 在实时图像识别方面面临挑战.
- 端口环境存在复杂的操作条件和对IGV硬件计算能力的限制.
研究的目的:
- 开发一种改进的IGV视觉导航检测方法.
- 为了提高IGV的图像识别和检测的准确性和实时性能.
- 为了解决传统特征提取和IGV硬件的局限性.
主要方法:
- 使用简化的YOLOv2 (SYOLOv2) 模型,完善其卷积神经网络结构.
- 实现了对比度有限的自适应直方图平衡 (CLAHE) 用于图像预处理.
- 提出了一个基于车辆速度的自适应图像分辨率检测模型.
主要成果:
- 与传统方法相比,SYOLOv2在精度和每秒 (FPS) 中表现出显著的改进.
- 该方法使用单个图像显示了适应不同天气条件的适应性.
- 在现实数据集上的实验证实了更好的检测性能.
结论:
- 拟议的SYOLOv2视觉导航检测方法有效地满足了对IGVs的严格实时检测要求.
- 这种方法提供了一个可行的解决方案,以提高IGV在具有挑战性的工业环境的感知.
- 这种精细的模型提高了自动驾驶汽车导航系统的稳定性和效率.
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