基于分组卷积与双注意力机制的河流水面污染图像分类

Image Classification of River Water Surface Pollution Based on Grouped Convolution and Dual Attention Mechanism

河流水面污染物是危害河流资源的重要污染物,及时发现并处理水面污染物可以有效地保护河流环境以及水资源,能进一步实现减污降碳,提升生态系统碳汇能力.随着智能化的大范围推广,传统的河流水面污染物的监测处理方法已经不能满足当今的需求.针对辽河流域水面污染问题,本文将计算机视觉技术应用到了河流水面污染分类上,提出了基于分组卷积与双注意力机制的河流水面污染图像分类算法模块(grouped convolution dual attention,GCDA),在分组卷积的基础上引入简化的双注意力机制,使用较少的参数量增强了网络对图像的特征提取能力,进一步提升图像分类效果.通过固定位截取图像的方式对辽河流域中的温泉城水站取水口、王营河入细河、高台子断面、津源污水排口和清源污水处理厂溢流口5个河流监控摄像图像做了预处理工作并建立了一个河流水面污染物数据集,图像分为污染和未污染两类,通过实验证明在此数据集上,添加使用GCDA模块的网络相较于原网络以及分别添加空间、通道注意力机制的网络在河流水面污染物图像的二分类任务中效果有明显提升.

Water surface pollutants of rivers are the main pollutants that endanger river resources. Timely detection and treatment of water surface pollutants can effectively protect the river environment and water resources and further boost the pollution and carbon reduction and the carbon sink capacity of the ecosystem. With the widespread promotion of intelligence, traditional monitoring and processing methods for water surface pollutants of rivers can no longer meet the current needs. To address water surface pollution in the Liaohe River basin, this study applies computer vision technology to the classification of water surface pollution and proposes a classification algorithm module based on grouped convolution and the dual attention(GCDA) mechanism for images of water surface pollution. Specifically, a simplified dual attention mechanism is introduced into the network on the basis of grouped convolution, which uses fewer parameters to enhance the network’s ability to extract features of images and further enhances the effect of image classification. The method of capturing images at a fixed position is performed on images from five river monitoring cameras in the Liaohe River basin for preprocessing. The five cameras refer to the ones in the hot spring intake of Chengshui Station, the confluence of Wangyinghe River and Xihe River, Gaotaizi Section, Jinyuan Sewage Outlet, and the overflow port of Qingyuan Sewage Treatment Plant. In addition, a dataset for water surface pollutants of rivers is established, and these images are categorized as polluted and unpolluted ones. Experiments indicate that compared with the original network and the network that adds space and channel attention mechanisms separately, the network with the GCDA module demonstrates better performance on this dataset in the dichotomous classification of images of water surface pollutants.