Proceeding the categorization of microplastics through deep learning-based image segmentation

Hui Huang; Huiwen Cai; Junaid Ullah Qureshi; Syed Raza Mehdi; Hong Song; Caicai Liu; Yanan Di; Huahong Shi; Weimin Yao; Zehao Sun

doi:10.1016/j.scitotenv.2023.165308

Proceeding the categorization of microplastics through deep learning-based image segmentation

Sci Total Environ. 2023 Oct 20:896:165308. doi: 10.1016/j.scitotenv.2023.165308. Epub 2023 Jul 4.

Authors

Hui Huang¹, Huiwen Cai², Junaid Ullah Qureshi³, Syed Raza Mehdi³, Hong Song³, Caicai Liu⁴, Yanan Di⁵, Huahong Shi², Weimin Yao⁴, Zehao Sun³

Affiliations

¹ Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China; Hainan Institute of Zhejiang University, Sanya 572024, Hainan, PR China.
² State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, PR China.
³ Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China.
⁴ East China Sea Bureau of the Ministry of Natural Resources, Shanghai 200137, PR China.
⁵ Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, PR China. Electronic address: diyanan@zju.edu.cn.

PMID: 37414186
DOI: 10.1016/j.scitotenv.2023.165308

Abstract

Microplastics (MPs) have been recognized as prominent anthropogenic pollutants that inflict significant harm to marine ecosystems. Various approaches have been proposed to mitigate the risks posed by MPs. Gaining an understanding of the morphology of plastic particles can provide valuable insights into the source and their interaction with marine organisms, which can assist the development of response measures. In this study, we present an automated technique for identifying MPs through segmentation of MPs in microscopic images using a deep convolutional neural network (DCNN) based on a shape classification nomenclature framework. We used MP images from diverse samples to train a Mask Region Convolutional Neural Network (Mask R-CNN) based model for classification. Erosion and dilation operations were added to the model to improve segmentation results. On the testing dataset, the mean F1-score (F1) of segmentation and shape classification was 0.7601 and 0.617, respectively. These results demonstrate the potential of proposed method for the automatic segmentation and shape classification of MPs. Furthermore, by adopting a specific nomenclature, our approach represents a practical step towards the global standardization of MPs categorization criteria. This work also identifies future research directions to improve accuracy and further explore the possibilities of using DCNN for MPs identification.

Keywords: Deep learning; Microplastics; Microscopic images; Shape classification.