Microplastic Identification Using AI-Driven Image Segmentation and GAN-Generated Ecological Context
This work addresses the need for more accessible and cost-effective microplastic detection for experts and citizens, though it is incremental as it builds on existing segmentation and GAN methods.
The paper tackles the problem of costly microplastic identification in water samples by developing a deep learning segmentation model that uses GAN-generated data to improve training, achieving an F1-Score of 0.91 compared to 0.82 without generated data.
Current methods for microplastic identification in water samples are costly and require expert analysis. Here, we propose a deep learning segmentation model to automatically identify microplastics in microscopic images. We labeled images of microplastic from the Moore Institute for Plastic Pollution Research and employ a Generative Adversarial Network (GAN) to supplement and generate diverse training data. To verify the validity of the generated data, we conducted a reader study where an expert was able to discern the generated microplastic from real microplastic at a rate of 68 percent. Our segmentation model trained on the combined data achieved an F1-Score of 0.91 on a diverse dataset, compared to the model without generated data's 0.82. With our findings we aim to enhance the ability of both experts and citizens to detect microplastic across diverse ecological contexts, thereby improving the cost and accessibility of microplastic analysis.