| 王 宇,魏 宇,孙传猛,武志博,李 勇.复杂恶劣环境下水位智能检测方法研究[J].电子测量与仪器学报,2023,37(11):119-131 |
| 复杂恶劣环境下水位智能检测方法研究 |
| Research on intelligent detection method of water level incomplex and harsh environment |
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| DOI： |
| 中文关键词: 复杂恶劣环境 水位智能检测 YOLOv5 水尺 |
| 英文关键词:complex and harsh environment intelligent detection of water level YOLOv5 water gauge |
| 基金项目:国家重点研发计划青年科学家项目(2022YFC2905700)、山西省基础研究计划项目(202203021212129,202203021221106)、山西省科技成果转化引导专项(202104021301061)资助 |
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| Author | Institution |
| Wang Yu | 1. State Key Laboratory of Dynamic Measurement Technology, North University of China,2. School of Electrical and Control Engineering, North University of China |
| Wei Yu | 1. State Key Laboratory of Dynamic Measurement Technology, North University of China,2. School of Electrical and Control Engineering, North University of China |
| Sun Chuanmeng | 1. State Key Laboratory of Dynamic Measurement Technology, North University of China,2. School of Electrical and Control Engineering, North University of China |
| Wu Zhibo | 1. State Key Laboratory of Dynamic Measurement Technology, North University of China,2. School of Electrical and Control Engineering, North University of China |
| Li Yong | 3. State Key Laboratory of Coal Mine Disaster Dynamics and Control,Chongqing University |
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| 摘要点击次数: 303 |
| 全文下载次数: 409 |
| 中文摘要: |
| 实现智能化水务管控和洪涝灾害预警,需要实时、准确感知水位信息变化情况。 针对现有技术不能满足夜晚、雾天、雨
天、漂浮物遮挡、灯光阴影等复杂恶劣环境下的水尺水位的影像水位反演(小目标特征)识别需求,提出一种融合改进 YOLOv5
与 RankSE 的水位智能检测方法。 首先,采用强化小尺度特征的多层级特征融合方法来改进 YOLOv5 算法,以强化对小目标的
捕捉能力;其次,融入 RankSE 模块进一步提升对小目标的感知能力;最后,提出一种全新的水位高程解算方案,仅需利用部分
水尺锚框信息即可获得准确的水位高程信息,极大提升了检测方法的鲁棒性。 研究结果表明,本文所述方法水位检测相对准确
度达 98. 5%,较原算法提高了 8. 4%;在复杂恶劣环境下可以自动、准确识别出水位高程,最大误差仅为 0. 11 m。 研究结果有效
提升了复杂恶劣环境下水位检测的准确性。 |
| 英文摘要: |
| To realize intelligent water management and control and flood disaster early warning, it is necessary to accurately sense the
change of water level information in real time. Because the prior technology cannot meet the requirements of water level identification in
complex and harsh environments such as night, fog, rainy day, floating object occlusion, light shadows, etc. , an intelligent water level
detection method based on improved YOLOv5 and RankSE was proposed. Firstly, the YOLOv5 algorithm was improved by the multi-level
feature fusion method which strengthens small-scale features, to strengthen the ability of capturing small targets. Secondly, integrating
the RankSE module further enhances the perception of small targets. Finally, a new solution of water level elevation was proposed, which
can obtain accurate water level elevation information only by using part of water gauge anchor frame information, which greatly improved
the robustness of the detection method. The research results show that the accuracy of water level detection in this paper reached 98. 5%,
which was 8. 4% higher than the original algorithm. The water level elevation could be automatically and accurately identified in complex
and harsh environments. The maximum error was only 0. 11 m. The research results effectively improve the accuracy of water level
detection in complex and harsh environments. |
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