基于联合特征定位的水文标尺识别方法

Water Gauge Measurment Based on Joint Feature Location

为解决现有基于视觉特征的水位测量方法在光照变化强烈以及成像质量模糊等恶劣环境下存在定位精度差和识别鲁棒性弱等问题,提出一种多特征联合定位的水尺识别方法。首先利用水尺的几何结构和颜色特征对水尺图像进行粗定位得到水尺候选区域,再提取候选区域的方向梯度直方图特征输入支持向量机得到水尺精定位,然后结合形态学算子和投影法实现字符和水尺刻度分割,通过自建水尺字符库来增强字符模型的泛化性能,最后采用LeNet-5框架对归一化、标准化字符进行识别并输出水尺识别结果。仿真结果表明,在不同视距和视角条件下水尺字符和刻度的识别准确率达到了99.4%,有效提高了恶劣环境下水尺识别的准确性和鲁棒性。

Water level measurement is one of the key issues of hydrological observation.To solve the problems of existing water gauge recognition methods which are poor positioning accuracy and recognition robustness in harsh environments,this paper proposes a multi-feature joint localization-based water gauge recognition method.Firstly,the geometric structure and color features of the water gauge are used to roughly locate the water gauge image to obtain candidate areas.Then,the directional gradient histogram features of the candidate areas are extracted and input into the support vector machine to obtain accurate water gauge positioning;Then,combining morphological operators and projection methods to achieve character and water gauge scale segmentation.Finally,the convolutional neural network LeNet5 is used to recognize normalized and standardized characters and output the water gauge recognition results.To improve the accuracy of water gauge recognition under different perspectives and line of sight conditions,a self built water gauge character library is used to enhance the generalization performance of the character model.Simulation results show that the proposed algorithm,which achieves the recognition accuracy of characters and scales about 99.4%,effectively improves the accuracy and robustness of water gauge recognition in harsh environments.