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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
Article Submitted : 24205
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Total Authors : 24597
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Published Paper Details
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| Paper Title: | Water Guardian : Intelligent Underwater Waste Detection |
| Authors Name: | Namya P V , Akshara K , Meghna C , Snigdha R K , Dinla O K |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_211122
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| Published Paper Id: | IJRTI2604260 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Underwater waste is a serious problem caused by human activities like fishing, shipping, tourism and dumping of garbage and plastics into rivers and oceans. Detecting underwater waste is difficult because of low visibility and deep or hidden locations. Unmanned Underwater Vehicles (UUVs) are cost- effective solutions for undersea monitoring but face significant challenges due to visual distortions caused by light absorption and scattering as well as limited onboard power resources. To overcome these issues, an intelligent two-stage framework has been developed that first employs an efficient deep learning model for detecting underwater objects and regions of interest (ROIs) such as fish, divers and submarines. The detected ROIs are then processed through an advanced image restoration algorithm that enhances visual quality supporting more reliable navigation and monitoring for resource-constrained UUVs. Building upon this foundation, the proposed system extends its capabilities by incorporating an underwater waste detection module designed to identify and classify non-biodegradable waste materials such as plastic bottles, tyres, face masks, gloves and selected categories of electronic waste (E-waste) including mobile adapters, mouse, keyboard, smartphones and TV remotes. The system supports image and video uploads as well as real-time inputs and integrates underwater image preprocessing techniques with specialized object detection algorithms to enable accurate recognition of waste objects, thereby enhancing underwater environmental monitoring and contributing to marine ecosystem protection. This integrated framework allows consistent detection performance across different input formats while maintaining reliable identification of non-biodegradable waste materials. As a result, the system provides a comprehensive solution for continuous and effective underwater waste monitoring. |
| Keywords: | Underwater Waste Detection, Deep Learning ,YOLO Object Detection, E-waste Detection ,Underwater Image Processing |
| Cite Article: | "Water Guardian : Intelligent Underwater Waste Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b900-b906, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604260.pdf |
| Downloads: | 000205504 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604260
Registration ID:211122
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b900-b906 Country: Kannur , Kerala, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604260 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604260 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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