用深度学习自动识别消费产品中的微纳米塑料
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
- 构建开源图像数据集MiNa，用于塑料颗粒检测与分类
- 在真实水环境模拟图像上验证算法性能，支持多尺度识别
- 适合环境监测、材料科学及人工智能交叉研究者
塑料污染正日益成为全球性问题，影响健康与生态环境，微塑料和纳米塑料已广泛存在于饮用水到空气等各类介质中。传统检测方法耗时耗力，亟需更高效的技术手段。本文提出一种名为MiNa的新颖开源数据集，专为基于目标检测算法的微塑料和纳米塑料自动检测与分类而设计。该数据集包含在真实水环境条件下模拟的扫描电子显微镜图像，涵盖多种聚合物类型与宽泛尺寸范围。我们展示了前沿检测算法在MiNa上的应用效果，评估其性能并揭示各方法的独特挑战与潜力。该数据集填补了微塑料研究领域关键资源空白，为未来技术发展提供坚实基础。
原文摘要 · Abstract (English)
Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums from potable water to air. Traditional methods for studying these contaminants are labor-intensive and time-consuming, necessitating a shift towards more efficient technologies. In response, this paper introduces micro- and nanoplastics (MiNa), a novel and open-source dataset engineered for the automatic detection and classification of micro and nanoplastics using object detection algorithms. The dataset, comprising scanning electron microscopy images simulated under realistic aquatic conditions, categorizes plastics by polymer type across a broad size spectrum. We demonstrate the application of state-of-the-art detection algorithms on MiNa, assessing their effectiveness and identifying the unique challenges and potential of each method. The dataset not only fills a critical gap in available resources for microplastic research but also provides a robust foundation for future advancements in the field.
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