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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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WCD-YOLO: A waste classification detection model.

Long Ling1, Yufeng Chen2, Zhiwu Li2

  • 1Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, 999078, Macao Special Administrative Region of China; School of Intelligent Manufacturing and Aeronautics, Zhuhai College of Science and Technology, Zhuhai, 519041, China.

Journal of Environmental Management
|January 11, 2026
PubMed
Summary
This summary is machine-generated.

A new WCD-YOLO model enhances intelligent waste classification with optimized feature extraction and a novel pyramid network. This low-consumption, high-precision model achieves superior accuracy in waste recognition.

Keywords:
Deep learningWCD-YOLOWaste classificationYOLOv10

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Environmental Science

Background:

  • Intelligent waste classification is crucial for environmental sustainability.
  • Existing models often struggle with feature extraction and precision for diverse waste types.

Purpose of the Study:

  • To develop an advanced waste classification detection model (WCD-YOLO).
  • To improve feature extraction, precision, and detection capabilities for waste recognition.

Main Methods:

  • Optimized YOLOv10 backbone with MCA module for enhanced feature extraction.
  • Introduced FNC2f module for efficient, multi-scale feature enrichment.
  • Designed FNC2f-BiFPN for improved detection of waste with limited features.
  • Utilized Inner-CIoU loss function and controlled auxiliary boundary scale.

Main Results:

  • WCD-YOLO achieved mAP50 of 95.8% (1.6% increase) and mAP50:95 of 74.0% (2.6% increase).
  • The model boasts low parameters (7.2MB) and GFLOPs (8.5G).
  • Demonstrated superior precision over other models on a self-built dataset.

Conclusions:

  • WCD-YOLO offers a high-precision, low-consumption solution for intelligent waste classification.
  • The model provides a valuable reference for future research and engineering in waste management.
  • Optimized architecture significantly improves waste recognition accuracy.