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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Sep 16, 2025

Cross-Modal Multivariate Pattern Analysis
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CM-YOLO: A Multimodal PCB Defect Detection Method Based on Cross-Modal Feature Fusion.

Haowen Lan1, Jiaxiang Luo1, Hualiang Zhang2

  • 1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study introduces CM-YOLO, a new method for printed circuit board defect detection that combines RGB and depth images. It significantly improves detection accuracy by enhancing feature perception and fusion.

Keywords:
PCB defect detectionfeature fusionmultimodal

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Defect detection on printed circuit boards (PCBs) is crucial for manufacturing quality.
  • Existing algorithms struggle with subtle defects, necessitating enhanced feature perception.
  • Integrating multimodal data (RGB and depth images) offers potential for improved robustness.

Purpose of the Study:

  • To propose a novel weighted feature fusion method for enhanced PCB defect detection.
  • To improve the robustness and reliability of defect detection algorithms.
  • To leverage differential amplification principles for multimodal feature fusion.

Main Methods:

  • Developed CM-YOLO, a dual-stream detection network incorporating a Differential Amplification Weighted Fusion (DAWF) module.
  • DAWF separates multimodal features into common-mode and differential-mode for enhanced characteristic preservation.
  • Integrated a Cross-Attention Spatial and Channel (CASC) module to boost feature extraction capabilities.

Main Results:

  • The CM-YOLO method achieved a mean Average Precision (mAP) of 0.969 in experiments.
  • Demonstrated superior accuracy and effectiveness compared to existing methods.
  • Validated the enhanced feature perception and fusion capabilities.

Conclusions:

  • CM-YOLO offers a highly accurate and effective solution for PCB defect detection.
  • The proposed DAWF and CASC modules significantly contribute to improved detection performance.
  • The integration of RGB and depth images via weighted feature fusion enhances algorithm reliability.