一种基于田间障碍物检测的车距预警方法、系统及设备
By improving the combination of the YOLO v5 model and the Lite-Mono depth estimation model, and using RGB cameras for field obstacle detection and distance estimation, the problems of inaccurate detection and complexity in existing technologies are solved, and efficient obstacle warning is achieved.
CN117351461BActive Publication Date: 2026-07-17CHINA AGRI UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA AGRI UNIV
- Filing Date
- 2023-10-19
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing field obstacle detection methods cannot provide accurate distance information, and multi-sensor fusion-based methods are costly and complex.
Method used
An improved YOLO v5 model combined with a Lite-Mono depth estimation model is used to acquire images via an RGB camera. Channel pruning and calibration parameters are then applied, and the system is deployed on a Jetson Xavier NX embedded system for obstacle detection and distance estimation.
Benefits of technology
It enables accurate detection and distance estimation of obstacles in the field, reducing the complexity of the system and the difficulty of implementation.
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Figure CN117351461B_ABST
Abstract
本发明公开一种基于田间障碍物检测的车距预警方法、系统及设备，涉及图像处理领域，该方法包括基于RGB相机采集多种田间类别障碍物图像，构建障碍物数据集；利用障碍物数据集训练改进的YOLO v5模型；改进的YOLO v5模型为在YOLO v5模型的基础上进行通道剪枝处理；将训练好的改进的YOLO v5模型与Lite‑Mono深度估计模型部署在Jetson XavierNX嵌入式系统；根据障碍物检测结果图像，采用Lite‑Mono深度估计模型结合校准参数对障碍物的距离进行估计，得到障碍物类型和距离信息；根据障碍物类型和距离信息进行预警。本发明能够实现对田间障碍物的准确检测和距离估计，并降低了实施的复杂性和实施难度。
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