摘要
为了实现基于视频图像的水位自动监测,以及解决传统视频监测算法环境适应性差和鲁棒性低的问题,提出一种基于语义分割的视频水位监测方法。采用改进的DeepLabv3+算法并结合空间注意力机制、通道注意力机制和边缘细化模块对水位标尺图像进行分割,用来提取水平面坐标,根据相机标定结果的线性插值来计算实际水位值。实验结果表明,所提算法在水位标尺数据集上的平均交并比达到97.18%,优于DeepLabv3+和BiSeNet(Bilateral Segmentation Network)等语义分割算法;所提算法的检测平均像素误差率为0.76%,在实测环境下水位读数误差小于1 cm。相较于现有的传统图像处理水位监测算法和基于深度学习的水位监测算法,所提算法的环境适应性更强,鲁棒性更高,读数更精准,能够较准确地实现水体水位的自动监测。
In order to realize automatic water level monitoring based on video images and solve the problems of poor environmental adaptability and low robustness of traditional video monitoring algorithms, a video water level monitoring method based on semantic segmentation is proposed. The improved DeepLabv3+ algorithm, combined with spatial attention mechanism, channel attention mechanism and edge refinement module, is used to segment the water level scale image to extract horizontal coordinate, and the actual water level value is calculated according to the linear interpolation of camera calibration results. The experimental results show that the average intersection ratio of the proposed algorithm on the water level scale dataset reaches 97. 18%, which is better than DeepLabv3+ and BiSeNet(Bilateral Segmentation Network) semantic segmentation algorithms. The average pixel error rate of the proposed algorithm is 0. 76%, and the error of water level reading is less than 1 cm in the measured environment.Compared with the existing traditional image processing water level monitoring algorithm and water level monitoring algorithm based on deep learning, the proposed algorithm has stronger environmental adaptability, higher robustness, more accurate reading, and can achieve more accurate automatic water level monitoring.
作者
傅启凡
路茗
张质懿
纪立
丁华泽
Fu Qifan;Lu ming;Zhang Zhiyi;Ji Li;Ding Huaze(Key Laboratory of Wireless Sensor Network and Communication,Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 201800,China;University of Chinese Academy of Sciences,Beijing 100864,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第4期81-92,共12页
Laser & Optoelectronics Progress
基金
国家重点研发计划(2018YFC1505204)。
关键词
图像处理
数字图像处理
水位测量
语义分割
注意力机制
边缘细化
image processing
digital image processing
water level measurement
semantic segmentation
attention mechanism
edge refinement
