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Related Concept Videos

Lumber Defects01:23

Lumber Defects

Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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Leaky Scanning

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LC Circuits01:21

LC Circuits

An LC circuit consists of an inductor and a capacitor, either in series or parallel. Consider a charged capacitor connected with an inductor in series. Before the switch is closed, all the energy of the circuit is stored in the electric field of the capacitor. When the switch is closed, the capacitor begins to discharge, producing a current in the circuit. The current, in turn, creates a magnetic field in the inductor. Because of the induced emf in the inductor, the current cannot change...
IR Frequency Region: Fingerprint Region01:03

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Related Experiment Videos

FMW-YOLO: A Frequency-Enhanced and Multi-Scale Context-Aware Framework for PCB Defect Detection.

Yuguo Li1, Shuo Tian1, Wenzheng Sun1

  • 1College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271019, China.

Micromachines
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

FMW-YOLO enhances printed circuit board (PCB) defect detection using a novel lightweight framework. This approach improves accuracy and reduces parameters for reliable electronic system inspection.

Keywords:
Channel-Transposed attentionPCB defect detectionfrequency enhancementmulti-scale features

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Manufacturing Technology

Background:

  • Printed circuit board (PCB) defect detection is crucial for electronic system reliability.
  • Conventional methods struggle with complex backgrounds and small-scale defects, limiting precision.
  • Existing approaches often lack efficiency and accuracy in real-world industrial settings.

Purpose of the Study:

  • To propose FMW-YOLO, a lightweight and accurate detection framework for high-precision PCB surface defect inspection.
  • To enhance feature extraction and representation for improved detection of small and complex defects.
  • To optimize the detection framework for practical industrial applications with reduced computational burden.

Main Methods:

  • Developed a Frequency-Enhanced Channel-Transposed and Local Feature backbone network for superior feature extraction.
  • Introduced a Dual-Frequency and Channel Attention Aggregation module and Lightweight Edge-Gaussian Block to refine feature representation.
  • Incorporated a Multi-Scale Context-Aware Enhancement mechanism with an Attention-Integrated Feature Pyramid in the neck network.
  • Utilized a Dilated Reparam Residual Module to expand receptive fields efficiently.
  • Employed Wise-IoU for optimized bounding box regression.

Main Results:

  • FMW-YOLO achieved improved mean Average Precision at 50% IoU (mAP50) by 2.1% on the HRIPCB dataset and 0.3% on the DeepPCB dataset.
  • The proposed method reduced the number of parameters by 23% compared to baseline models.
  • Demonstrated enhanced suppression of noise interference while preserving high-frequency details crucial for defect detection.

Conclusions:

  • FMW-YOLO offers a significant advancement in lightweight and accurate PCB surface defect detection.
  • The framework effectively addresses challenges posed by complex backgrounds and small-scale defects.
  • The method shows strong potential for practical industrial applications, improving inspection efficiency and reliability.