International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Implementation Paper on Detection of Plastic Debris in River Environments using Deep Learning |
|---|---|
| ABSTRACT | Plastic pollution in river environments has emerged as a critical ecological concern, with rivers serving as primary pathways for transporting waste from land to oceans. Traditional monitoring methods are manual, time-consuming, and unable to provide timely assessments. This project presents RiverGuard AI, a deep learning-based system for automated detection of plastic debris in river imagery. The dataset, sourced from Roboflow, consists of annotated images of plastic waste under various environmental conditions. The YOLOv8n model was trained in Google Colab and integrated into a Python Flask web application with an HTML/CSS/JavaScript frontend. Users can upload river images through the browser interface and receive real-time detection results including annotated bounding boxes, plastic count, AI confidence score, and a severity-based remediation suggestion. The system demonstrated successful detection across varying pollution levels, classifying results as Clean, Mild, Moderate, or Critical with corresponding cleanup recommendations. The proposed system shows that deep learning, combined with structured datasets and lightweight web frameworks, can deliver an effective and accessible solution for automated river plastic monitoring. |
| AUTHOR | PROF. V.S.SONAWANE, BHARGAVI BHAGWAT, SANSKRUTI KOTHARI, HARSHALA GAIKWAD Department of Artificial Intelligence & Machine Learning, AISSMS's Polytechnic, Pune, Maharashtra, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403068 |
| pdf/68_Implementation Paper on Detection of Plastic Debris in River Environments using Deep Learning.pdf | |
| KEYWORDS | |
| References | 1. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2. Jocher, G., Chaurasia, A., & Qiu, J. (2023). Ultralytics YOLOv8. Retrieved from https://github.com/ultralytics/ultralytics 3. Majchrowska, S., Mikołajczyk, A., Ferlin, M., Klawikowska, Z., Plantykow, M., Kwasigroch, A., & Majek, K. (2022). Deep learning-based waste detection in natural and urban environments. Waste Management, 138, 274–284. 4. Tharani, S., Maheshwari, A., & Sharma, P. (2023). Detection of Marine Plastic Waste Using YOLOv5 and Drone Imagery. International Journal of Environmental Science and Technology. 5. Dwyer, J., & Roboflow Team. (2023). Roboflow: A Platform for Computer Vision Dataset Management. Retrieved from https://roboflow.com 6. Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python (2nd ed.). O'Reilly Media. 7. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436–4. |
