Intelligent image recognition and positioning of water pollution in river regulation projects based on UAV aerial photography

Abstract

Construction wastewater of small watersheds regulation projects cause river pollution. As a long-line project, river regulation often shows a scattered distribution, with inconvenient transportation, manual inspection is difficult to guarantee the efficiency of construction water pollution control. At present, intelligent inspection is usually used to identify floating objects, which is difficult to identify water pollution such as sewage and water bloom. This paper proposed an intelligent recognition and positioning method for anomaly images of water pollution in river regulation projects based on UAV aerial photography and deep learning. The aerial images of working area are collected by UAV inspection, and an EfficientNet-B0 model of intelligent identification and classification of construction water pollution image is established based on the MBConv module optimized by the SENet attention mechanism. Transfer learning is used to train the model, which can make the pollution-related features extracted by the model more directional, and improve the training speed and accuracy of the model. Then, the feature map of pollution area based on Grad-CAM(Class Activation Mapping) and fully connected CRF(Conditional Random Fields) can modify the misclassification area that achieve more refined fast marking and positioning of pollution area.The practical application result show that the classification accuracy of water pollution images is 98% and the evaluation index ORI of the positioning method is 96.91%. This study can provide advanced technical means for project managers to quickly control the water pollution of long-line river regulation projects.

Keywords

river regulation / construction-caused water pollution / image recognition / image positioning / UAV aerial photography / feature visualization