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Waste classification and management are important for healthier planet Earth. In this paper we are proposing an integrated approach for waste detection and classification using object detection along with natural language processing (NLP) techniques. Which introduce a YOLO-based model to detect and classify waste in images by using Bootstrap Language-Image Pretraining (BLIP) for scene understanding and contextual analysis. The workflow involves, feeding the waste images into a preprocessing stage (image), captioning image data with Natural Language Processing (NLP) to produce descriptive captions, and analyzing the textual features of detected captions that exist in the waste (waste elements). The classification of the detected object is performed by a custom trained YOLOv8 model which is fine-tuned on a specific waste class dataset. Experiments show that the model recognizes garbage, recyclables and litter with high accuracy. This system showcases the potential of combining visual and textual modalities to enhance waste detection accuracy, offering a robust tool for automated environmental monitoring and management.

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Integrated Object Detection and Scene Analysis for Waste Classification Using YOLO and NLP Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • M. Chaitanya Raju,
  • Maddu Reshma,
  • V. Anvesh,
  • Lekha S. Nair

摘要

Waste classification and management are important for healthier planet Earth. In this paper we are proposing an integrated approach for waste detection and classification using object detection along with natural language processing (NLP) techniques. Which introduce a YOLO-based model to detect and classify waste in images by using Bootstrap Language-Image Pretraining (BLIP) for scene understanding and contextual analysis. The workflow involves, feeding the waste images into a preprocessing stage (image), captioning image data with Natural Language Processing (NLP) to produce descriptive captions, and analyzing the textual features of detected captions that exist in the waste (waste elements). The classification of the detected object is performed by a custom trained YOLOv8 model which is fine-tuned on a specific waste class dataset. Experiments show that the model recognizes garbage, recyclables and litter with high accuracy. This system showcases the potential of combining visual and textual modalities to enhance waste detection accuracy, offering a robust tool for automated environmental monitoring and management.