用AI自动识别水样微塑料，提升检测效率与可及性
Microplastic Identification Using AI-Driven Image Segmentation and GAN-Generated Ecological Context
- 结合图像分割与GAN生成数据，自动识别微塑料
- 综合数据训练下模型F1分数达0.91，优于无生成数据的0.82
- 适合环境监测人员与公众参与微塑料调查
当前水样中微塑料的识别方法成本高且依赖专家分析。本文提出一种深度学习分割模型，用于自动识别显微镜图像中的微塑料。基于摩尔塑料污染研究中心的微塑料图像进行标注，并采用生成对抗网络（GAN）生成多样化的训练数据以扩充样本。为验证生成数据的有效性，我们进行了阅片研究，专家仅能以68%的准确率区分生成与真实微塑料。使用混合数据训练的模型在多样化数据集上获得0.91的F1分数，而未使用生成数据的模型仅为0.82。本研究旨在提升专家与公众在多种生态情境下检测微塑料的能力，从而降低分析成本并提高可及性。
原文摘要 · Abstract (English)
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.
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