An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks
An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks
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DOI：
10.3390/rs14236023
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发表时间：
2022-12-01
期刊：
影响因子：
5
通讯作者：
Pei, Qingqi
中科院分区：
文献类型：
--
作者：
Chen, Chen;Fu, Rufei;Pei, Qingqi
水利人员通常需要通过水位计图像以预期的精度实时了解水位。然而，从水位计图像中准确识别水位仍然是一个复杂的问题。本文提出了一种应用于中国江西省婺源市的复合方法。该方法能够对复杂多变的场景中的水位区域和数字区域进行检测，准确地检测出各种水位计的水位线，最终获得准确的水位值。首先，FCOS通过融合上下文调整模块进行改进，以满足边缘计算的要求并保证相当的检测精度。其次，针对水位特征不明显的场景，我们还应用Deeplabv3+的上下文调整模块来分割水面以上的水位区域。然后，可以利用该面积来获取水位线的位置。最后结合前两步的结果计算出水位值。详细的实验证明该方法解决了复杂水文场景中的水位识别问题。此外，该方法对水位的识别误差小于1厘米，证明其能够应用于真实的河流场景。
Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes.