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 | AI-Powered Autonomous Water Surface Garbage Detection and Cleaning using Centernet Model for Urban Environment Sustainability |
|---|---|
| ABSTRACT | Rivers and canals flowing through cities are often used illegally for dumping trash that contaminates fresh water channels, causes block age in sewerage leading to urban flooding. The dumped trash is often found floating on the water surface. Water in security (quantity and quality) affects sheath and live lihoods. Contaminated water causes1.7 million fatalities from readable diseases annually. The existing water surface floating garbage detection techniques cannot easily detect floating garbage in conditions involving complicated backdrops, small size shares, and other variables. Traditional water surface garbage detection algorithm shaves the short comings of low detection accuracy, slow speed, and susceptibility to interference. The AI for water surface garbage cleanings are emerged as new system. Therefore, this project proposes a new single-stage object detection model called Center Net, which is divided into three parts: feature extraction network, feature fusion network, and detection head. Hence this project employs the Center Net model to detect three kinds of water surface garbage, including plastic bottles, plastic bags, and plastic cups. First, a pyramid anchor generation approach is proposed, which makes the chart be generated centrally near the target and reduces the interference of background information in the anchor generation. This approach generates feature maps with a higher resolution and more distinct features, thereby enhancing the feature information of small targets and enhancing the classification accuracy. Meanwhile, it can greatly improve the efficiency of the waste cleaning industry and make outstanding contributions to protecting the water surface environment. |
| AUTHOR | R. KARTHIKEYAN, S. MANO |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311081 |
| pdf/81_AI-Powered Autonomous Water Surface Garbage Detection and Cleaning using Centernet Model for Urban Environment Sustainability.pdf | |
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