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In a balanced four-wire wye-to-wye system, the arrangement involves wye-connected sinusoidal voltage sources and loads, connected through a neutral wire that links the neutral nodes of the source and load. The load impedance is connected across each phase of the load. The wye-connected source can be connected to the wye-connected load in four-wire and three-wire arrangements. A three-phase system is considered balanced when the load on each phase is equal, leading to uniform current flow and...
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Circuit elements are the basic building blocks of an electric circuit. Essentially, an electric circuit is the interconnection of these elements. Within electric circuits, one can find two types of elements: passive and active. Active elements have the ability to generate energy, whereas passive elements do not. Passive elements include components like resistors, capacitors, and inductors, while active elements typically encompass generators, batteries, and operational amplifiers.
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EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Shiyi Luo1, Fang Wan1, Guangbo Lei1

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

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Summary
This summary is machine-generated.

This study introduces EC-YOLOv7, an enhanced YOLOv7 network for detecting electronic components (ECs) on printed circuit boards (PCBs). The new model significantly improves detection accuracy and speed for efficient PCB recycling.

Keywords:
PCBdeep learningelectronic componentsobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Materials Science (Recycling)

Background:

  • Electronic components (ECs) are crucial for printed circuit boards (PCBs).
  • Accurate EC detection and classification are vital for effective PCB recycling.
  • Traditional methods struggle with speed and accuracy due to EC variety and quantity.

Purpose of the Study:

  • To develop an enhanced YOLOv7 network (EC-YOLOv7) for improved EC detection on PCBs.
  • To address limitations of traditional methods in speed, performance, and accuracy.
  • To enhance the efficiency of electronic component recognition for recycling.

Main Methods:

  • Proposed EC-YOLOv7 network integrating ACmix modules into the E-ELAN architecture.
  • Implemented branch links and 1x1 convolutional arrays for faster feature retrieval.
  • Engineered ResNet-ACmix, improved SPPCSPS block, and utilized DyHead for enhanced spatial information capture.
  • Introduced WIoU-Soft-NMS for improved bounding-box regression and localization accuracy.

Main Results:

  • EC-YOLOv7 achieved a mean average precision (mAP@0.5) of 94.4% on the PCB dataset.
  • Demonstrated superior performance and higher frames per second (FPS) compared to the original YOLOv7 model.
  • Outperformed other common EC detection methods in experimental evaluations.

Conclusions:

  • The enhanced EC-YOLOv7 network significantly improves high-density EC target recognition.
  • The proposed model offers a more efficient and accurate solution for electronic component detection in PCB recycling.
  • EC-YOLOv7 represents a substantial advancement in automated PCB recycling processes.