复杂恶劣环境下水位智能检测方法研究

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。

Research on intelligent detection method of water level in complex and harsh environment

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.