结合双线性网络和注意力的蓝藻图像识别方法OA

Addressing issues such as the uneven quantity of cyanobacteria images collected in practical engineering environments,the influence of complex lighting conditions and the incomplete capture of local features in cyanobacteria images,a cyanobacteria image recognition method combining bilinear networks and attention mechanisms is proposed.Firstly,a cyanobacteria image dataset is developed and optimized using an image enhancement algorithm.Subsequently,bilinear networks are employed to comprehensively extract feature information from cyanobacteria images.Simultaneously,the convolutional block attention mechanism is integrated to emphasize important local features while disregarding irrelevant information.This approach aims to further improve the classification performance across four distinct types of cyanobacteria image including cyanobacteria-free,granular cyanobacteria,banded cyanobacteria and flake cyanobacteria.Experimental results conducted on the constructed Algea-ultimate cyanobacteria dataset demonstrate a notable enhancement in recognition accuracy compared to the classical ResNet18 model,with a 7.29%improvement.Furthermore,the proposed method has been implemented in the Taihu Lake Basin water quality monitoring and early warning platform to facilitate automated cyanobacteria image recognition,offering an intelligent solution for real-time monitoring of water body cyanobacteria morphology.