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Related Experiment Video

Updated: Jul 2, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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DSYOLO-trash: An attention mechanism-integrated and object tracking algorithm for solid waste detection.

Wanqi Ma1, Hong Chen1, Wenkang Zhang2

  • 1School of Business, Jiangnan University, Wuxi 214122, PR China; Research Institute of National Security and Green Development, Jiangnan University, Wuxi 214122, PR China.

Waste Management (New York, N.Y.)
|February 20, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed DSYOLO-Trash, an intelligent system for real-time solid waste identification and tracking. This advanced model simplifies recycling, contributing to sustainable urban waste management.

Keywords:
Attention mechanismObject detectionWaste detectionWaste management

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Area of Science:

  • Environmental Science
  • Computer Science
  • Robotics

Background:

  • Urban solid waste production varies globally with living standards, posing diverse management challenges.
  • Current waste recycling standards are often too complex for public adherence.
  • Effective waste management is crucial for urban sustainability.

Purpose of the Study:

  • To develop an intelligent system for accurate and real-time solid waste identification and tracking.
  • To simplify waste classification and improve recycling accessibility for the public.
  • To contribute to intelligent waste management solutions for sustainable cities.

Main Methods:

  • Proposed DSYOLO-Trash model integrating dual attention mechanisms: Convolutional Block Attention Module (CBAM) and Contextual Transformer Networks (CotNet).
  • Applied Deep Simple Online and Realtime Tracking (DeepSORT) for the first time in solid waste detection for real-time identification and tracking.
  • Developed the MMTrash dataset, a multi-label dataset of mixed solid waste simulating real-world scenarios.
  • Integrated the improved You Only Look Once (YOLO) algorithm with DeepSORT, industrial cameras, and PLC-controlled robotic arms for automated waste sorting.

Main Results:

  • DS YOLO-Trash demonstrated superior performance compared to classical detection algorithms on both MMTrash and TrashNet datasets.
  • The system achieved real-time identification and tracking of solid waste.
  • The integration of attention mechanisms and DeepSORT enhanced feature mining and optimized the learning process.

Conclusions:

  • The DSYOLO-Trash system offers a significant advancement in intelligent waste management.
  • The proposed model and dataset provide a robust solution for real-time waste classification.
  • This work contributes to the sustainable development of cities through improved waste sorting technologies.