Deep learning based approach for automated characterization of large marine microplastic particles

摘要

The rapidly growing concern of marine microplastic pollution has drawn attentions globally. Microplastic par-ticles are normally subjected to visual characterization prior to more sophisticated chemical analyses. However, the misidentification rate of current visual inspection approaches remains high. This study proposed a state-of-the-art deep learning-based approach, Mask R-CNN, to locate, classify, and segment large marine microplastic particles with various shapes (fiber, fragment, pellet, and rod). A microplastic dataset including 3000 images was established to train and validate this Mask R-CNN algorithm, which was backboned by a Resnet 101 architecture and could be tuned in less than 8 h. The fully trained Mask R-CNN algorithm was compared with U-Net in characterizing microplastics against various backgrounds. The results showed that the algorithm could achieve Precision = 93.30, Recall = 95.40, F1 score = 94.34, APbb (Average precision of bounding box) = 92.7, and APm (Average precision of mask) = 82.6 in a 250 images test dataset. The algorithm could also achieve a processing speed of 12.5 FPS. The results obtained in this study implied that the Mask R-CNN algorithm is a promising microplastic characterization method that can be potentially used in the future for large-scale surveys.