采用雷视融合方法的灌溉风险区异物入侵风险预警
Early warning of foreign object intrusion risk in irrigation risk areas using the mine-view fusion method
陈晓燕 1王川 2齐明杰 1张宁 2林晓龙 1霍延强 3刘世杰 4田源3
作者信息
- 1. 济南市水利工程服务中心,山东 济南 250013
- 2. 山东高速集团有限公司,山东 济南 250014
- 3. 山东大学齐鲁交通学院,山东 济南 250002
- 4. 山东大学微电子学院,山东 济南 250101
- 折叠
摘要
针对传统灌溉渠异物入侵监测方法检测精度低、时效性差、夜间巡检不全面、危险性高等问题,提出一种基于雷视融合的灌溉区异物入侵监测方法.针对灌溉区周边行人、动物等小目标误检及特征提取能力不足等问题,提出一种基于YOLOv5改进的小目标识别算法,提高对灌溉区周边小目标检测能力.通过实际场景测试试验,本研究提出的灌溉区雷视融合监测方法和改进的基于YOLOv5 的小目标识别算法,识别精确度达到 93.26%,监测范围是设备周围 360°,有效提升了不同时间段下的异物入侵监测能力,验证了该方法的准确性.
Abstract
Aiming at the problems of traditional detection of foreign body intrusion in irrigation canals,such as low detection accuracy,poor timeliness and incomplete inspection at night,this study proposed a detection system for foreign body intrusion in irrigation areas based on lightning vision fusion.Meanwhile,aiming at the problems of false detection and insufficient feature extraction ability of small targets such as pedestrians and animals around the irrigation area,this study proposed a small target detection algorithm improved by YOLOv5 to improve the detection ability of small targets around the irrigation area.Through the actual scene test,the proposed detection method of lightning fusion in the irrigation area and the improved small target detection algorithm effectively improved the detection ability of foreign body intrusion and verified the accuracy of the method.
关键词
激光雷达/摄像机/融合/灌溉区目标检测/小目标检测Key words
LiDAR/the video camera/integration/target detection in irrigation area/small target detection引用本文复制引用
基金项目
山东省重点研发计划(2020CXGC010118)
出版年
2024
NETL
NSTL
万方数据