BROWSER-BASED OBJECT DETECTION SYSTEM FOR ISOLATING PLASTIC BOTTLES USING THE COCO-SSD MODEL

Plastic waste, especially in urban environments and water bodies, poses a significant environmental threat. This paper presents a browser-based object detection system for identifying and isolating plastic bottles using state-of-the-art machine learning models. The system leverages TensorFlow.js, ML5.js, and P5.js libraries along with the COCO-SSD model to detect plastic bottles in real time using a mobile camera interface. By employing a browser-based architecture, the system offers cross-platform functionality, eliminating the need for server-based computations or specialized hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.

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Publication Details

Journal
International Journal of Advanced Research in Computer Science
Published
2024-10-20
DOI
https://doi.org/10.26483/ijarcs.v15i5.7124
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

BROWSER-BASED OBJECT DETECTION SYSTEM FOR ISOLATING PLASTIC BOTTLES USING THE COCO-SSD MODEL

Chidozie Managwu
International Journal of Advanced Research in Computer Science
Industrial Vision Systems and Defect Detection
article

BROWSER-BASED OBJECT DETECTION SYSTEM FOR ISOLATING PLASTIC BOTTLES USING THE COCO-SSD MODEL

Chidozie Managwu
article en

Abstract

Plastic waste, especially in urban environments and water bodies, poses a significant environmental threat. This paper presents a browser-based object detection system for identifying and isolating plastic bottles using state-of-the-art machine learning models. The system leverages TensorFlow.js, ML5.js, and P5.js libraries along with the COCO-SSD model to detect plastic bottles in real time using a mobile camera interface. By employing a browser-based architecture, the system offers cross-platform functionality, eliminating the need for server-based computations or specialized hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.

International Journal of Advanced Research in Computer ScienceVol. 15(5)
University of Bradford (GB), University of Pittsburgh at Bradford (US)
Sustainable cities and communities
Openalex Percentile: Top 73%
Industrial Vision Systems and Defect Detection
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