Deep Neural Network-Based Electron Microscopy Image Recognition for Source Distinguishing of Anthropogenic and Natural Magnetic Particles

作者：Lin Liu, Tianyou Chen, Qinghua Zhang, Weican Zhang, Hang Yang, Xiaoguang Hu, Jin Xiao, Qian Liu, Guibin Jiang · 发表于：Environmental Science & Technology · 年份：2023 · DOI：10.1021/acs.est.3c05252 · 被引用次数：15 · 研究领域：Advanced Neural Network Applications、Electron and X-Ray Spectroscopy Techniques、Geophysical and Geoelectrical Methods

Deep learning models excel at image recognition of macroscopic objects, but their applications to nanoscale particles are limited. Here, we explored their potential for source-distinguishing environmental particles. Transmission electron microscopy (TEM) images can reveal distinguishable features in particle morphology from various sources, but cluttered foreground objects and scale variations pose challenges to visual recognition models. In this proof-of-concept work, we proposed a novel instance segmentation model named CoMask to tackle these issues with atmospheric magnetic particles, a key species of PM 2.5 . CoMask features a densely connected feature extraction module to excavate multiscale spatial cues at the single-particle level and enlarges the receptive field size for improved representation capability. We also employed a collaborative learning strategy to further improve performance. Compared with other state-of-the-art models, CoMask was competitive on benchmark and TEM data sets. The application of CoMask not only enables the source-distinguishing of magnetic particles but also opens up a new vista for machine learning applications.