Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater

A Multi-View Image Feature Fusion Network Applied in Analysis of Aeration Velocity for WWTP

The instability of the aeration system brings a significant challenge to the management of wastewater treatment plants (WWTP). Using image recognition methods to monitor aeration conditions accurately and enhance management efficiency is a promising way to solve this problem. To improve the efficiency of aeration condition identification and provide support for troubleshooting, we propose a method for aeration velocity condition identification based on a multi-view image feature fusion network (MVNN). Firstly, an experimental platform for simulating aeration tanks is established, and two cameras are used to acquire aeration images from different perspectives. Secondly, an image data set with 10 aeration velocity gradients is constructed and applied to the network’s training. Finally, the MVNN is used to extract and fuse the features of aeration images, and the model’s performance is evaluated on the dataset. Experiments show that the average accuracy of the method is over 98.3%, and the AUC of aeration identification is above 0.98, which indicates that the model has the potential for practical application in WWTP.