通过深度学习方法检测生物废水处理过程中微粒和生物质之间的相互作用
Tianlong Jia1, Zhaoxu Peng2, Jing Yu3
1Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Water Management, Stevinweg 1, 2628 CN Delft, the Netherlands.
The Science of the total environment
|August 27, 2024
概括
一种新的深度学习 (DL) 方法自动检测废水处理中的微粒,大大提高了比传统的ImageJ软件的准确性和速度. 这一进步为生物系统中的粒子生物质相互作用提供了负担得起的见解.
科学领域:
- 环境科学与工程环境科学与工程
- 生物技术是生物技术.
- 计算机科学 计算机科学
背景情况:
- 了解有影响的粒子-生物质相互作用对于优化生物废水处理至关重要.
- 传统的显微镜图像分析,如ImageJ,是耗时的,昂贵的,需要大量的手动参数调整.
- 现有的方法难以准确有效地量化复杂生物质矩阵中的微粒.
研究的目的:
- 开发和验证深度学习 (DL) 方法,用于自动检测和定量生物废水处理中的微粒.
- 将DL方法的性能与传统的ImageJ处理软件进行比较.
- 为分析微粒子-生物质相互作用提供一个经济实惠和高效的替代方案.
主要方法:
- 介绍了"TU Delft-粒子与生物质之间的相互作用"数据集,包括标记的显微镜图像.
- 实施和评估七个最先进的深度学习架构 (例如,Mask R-CNN,Cascade Mask R-CNN,YOLOv8),例如细分.
- 比较DL模型性能 (Cascade Mask R-CNN with ResNet50) 与ImageJ进行检测准确性和处理时间的比较.
主要成果:
- 级联面具R-CNN在测试组上实现了高检测准确度,mAP50指标为90.6%.
- DL方法比ImageJ显著改进,微平均精度高13.8%,微平均回忆率高21.7%.
- 处理速度:DL方法在1分钟内处理了70张图像,而ImageJ的处理时间超过6小时.
结论:
- 拟议的深度学习方法为分析废水处理中的微粒子-生物质相互作用提供了高度准确和高效的替代方案.
- 这种方法显著减少了处理时间和成本,与传统的方法比如ImageJ.相比.
- DL方法为微粒转移提供了宝贵的见解,有助于优化生物处理系统.
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