基于两阶段深度学习的水位智能识别方法

Intelligent recognition algorithm for water level measurement based on improved YOLOX-S algorithm

针对目前水位检测存在误差大、缺乏水域环境普适性等问题,提出了一种基于两阶段深度学习的水位智能识别方法。该方法通过引入自适应空间特征融合(Adaptively Spatial Feature Fusion,ASFF)模块改进现有YOLOX-S算法的特征融合网络,提高特征信息融合强度,采用更加灵活的多项式损失(Poly Loss)对二元交叉熵损失(BCE Loss)进行优化,形成改进YOLOX-S模型。结合图像处理技术,建立了基于改进YOLOX-S模型的标准双色水尺和水尺“E”刻度识别的两阶段智能水位识别方法,有效提升了水位检测精度。实验结果表明,该方法第一阶段水尺和第二阶段水尺“E”刻度的平均识别率分别达98.94%和99.86%,最终计算水位的平均误差小于0.6 cm、极差误差小于0.8 cm,较传统典型水位识别方法分别减少1.98 cm和3.22 cm,实现了水位高精度智能识别,可为防汛抗旱决策提供有效的技术支撑。

In view of the problems of large errors and lack of universality in water level detection,a new two-stage deep learning based intelligent water level detection method was proposed.The method improved the feature fusion network of the existing YOLOX-S algorithm by introducing ASFF module to improve the intensity of feature information fusion,and optimized the binary cross entropy loss(BCE Loss)by using a more flexible polynomial loss(Poly Loss),forming an improved YOLOX-S model.Combined with traditional image processing technology,a two-stage intelligent water level recognition method based on the improved YOLOX-S model for standard double-color water level and water level"E"scale recognition was established,which effectively improved the accuracy of water level detection.Experimental results show that the average recognition rate of the first stage water level and the second stage water level"E"scale reaches 98.94%and 99.86%respectively.Moreover,the average error in calculating the water level is less than 0.6 cm and the range error is less than 0.8 cm,reducing by 1.98 cm and 3.22 cm respectively compared to traditional typical water level detection methods.The proposed method realizes high-precision intelligent recognition of water level and provides effective technical support for flood control and drought relief decision-making.