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 specialised hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.

Authors

Publication Details

Journal
British Journal of Computer Networking and Information Technology
Published
2024-10-02
DOI
https://doi.org/10.52589/bjcnit-it17pwut
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

Daniel Matthias, Chidozie Managwu, I. Kushchu
British Journal of Computer Networking and Information Technology
Industrial Vision Systems and Defect Detection
article

Browser-Based Object Detection System for Isolating Plastic Bottles using the COCO-SSD Model

Daniel Matthias, Chidozie Managwu, I. Kushchu
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 specialised hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.

British Journal of Computer Networking and Information TechnologyVol. 7(4)
Sustainable cities and communities
Openalex Percentile: Top 76%
Industrial Vision Systems and Defect Detection
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