Real-time quantitative monitoring method of cyanobacterial blooms in lake riparian zones based on video surveillance and application research
Excessive accumulation of cyanobacterial blooms in the riparian zones of lakes,which are the main source of drinking water,has a serious impact on water safety as well as on the ecological environment.Therefore,real-time quantitative monitoring of cyanobacterial blooms in lake riparian zones is a key initiative for the prevention and control of cyanobacterial blooms in lakes.Based on the images of waters captured by real-time video surveillance around the lake,a deep learning method was used for cyanobacteria pixel recognition,and the actual cyanobacteria bloom area corresponding to each cyanobacteria pixel was accurately calculated according to the camera imaging principle and internal and external parameters,and finally the total area of cyanobacteria bloom in the waters of the lake's riparian zone was statistically analyzed.The experimental results showed that the average intersection ratio and overall accuracy of the cyanobacterial pixel identification method based on the VGG16-UNet model reached 88.74％and 94.10％,respectively,which was better than similar methods;and the calculated values of cyanobacterial bloom area had a good fit with the measured values(R2=0.97).This method enabled the timely acquisition of cyanobacterial bloom coverage and facilitates monitoring the dynamic changes along the riparian zones of lakes,playing a pivotal role in the management of lake water environments.
国家科技期刊平台
NSTL
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