Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
- Title
- Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
- Creator
- Biju, Nandana; Ramasamy, Gobi
- Description
- Microplastic pollution is a growing environmental concern, threatening aquatic ecosystems and human health. This study presents a dual deep learning approach for microplastic detection and classification using two datasets. For water microplastics, YOLOv8 and YOLOv11 were employed for object detection. InceptionV3, VGG19, ResNet50, ResNet152, DenseNet121, EfficientNetB0, and a custom CNN were applied for classification, classifying three distinct microplastic types in non-aquatic environments. Experimental findings display high accuracy, and indicate the potential of AI-enabled solutions for environmental monitoring. This research contributes to SDG 6 Clean Water and Sanitation, promoting sustainable management of water. 2025 IEEE.
- Source
- Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- CNN; Deep Learning; Environmental Monitoring; Microplastic Detection; Object Detection; Water Pollution; YOLO
- Coverage
- Biju N., Christ (Deemed To Be University), Bangalore, India; Ramasamy G., Department of Computer Science, Christ University, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833152476-0;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Biju, Nandana; Ramasamy, Gobi, “Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25831.