基于改进的YOLOv5m的复杂环境水位检测方法
相关实验视频
Updated: Jun 14, 2025

06:37
Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
Published on: November 13, 2017
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Jiadong Li1, Chunya Tong1, Hongxing Yuan1
1School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种改进的YOLOv5m模型,用于准确识别水位,在复杂的现实条件下实现不到2厘米的误差. 该方法提高了实际水位识别应用的可靠性.
科学领域:
- 计算机视觉 计算机视觉
- 环境监测 环境监测
- 图像处理 图像处理
背景情况:
- 传统的水位识别方法在具有挑战性的环境中缺乏准确性.
- 这种限制阻碍了它们在现实场景中的实际应用和可靠性.
研究的目的:
- 开发一个强大而准确的水位识别系统.
- 在复杂的环境条件下克服现有方法的局限性.
主要方法:
- 一个改进的YOLOv5m模型与水位识别的上下文知识相结合.
- 适应值 卡尼操作员和霍夫转换用于图像偏斜校正.
- 基于细分标尺数据的水位值预测的线性回归.
主要成果:
- 拟议的方法实现的平均误差小于2厘米.
- 在各种现实环境中表现出高准确度,包括不同的光线,遮蔽,白天和夜晚.
- 成功提取和完善水位计信息.
结论:
- 这种新的方法显著提高了水位识别的准确性和可靠性.
- 该系统由于其稳定性,对现实世界的应用非常实用.
- 这种方法为环境水位监测提供了可靠的解决方案.
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