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Research on PCB Small Target Defect Detection Based on Improved YOLOv5
ID：15 View Protection：PUBLIC Updated Time：2022-12-19 13:38:52 Hits：1003 Poster Presentation

Start Time：Pending（Asia/Shanghai）

Duration：Pending

Session：No Session »

Presentation File
Abstract
 As global automation accelerates, the importance of the PCB as a core component of electronic products grows with each passing day. The smallest hazards in PCBs can cause huge losses. To address the high level of integration, miniaturization, and multilayering of PCB production technology, we are using a new and improved model based on YOLOv5 to detect PCB defects. This new model solves the problems of difficult feature extraction, the similarity between features, and poor detection performance of PCB defects.
Keywords
PCB defect detection, YOLOv5, Clustering algorithm, Attention mechanism, Decoupled-head
Speaker
Mou Liang
Hunan University of student

Liang Mou is a postgraduate student at Hunan University of Science and Technology, whose main research interests are deep learning, computer vision, and target detection.

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Important Date

  • Conference Date

    Nov 30

    2022

    to

    Dec 02

    2022

  • Nov 30 2022

    Draft paper submission deadline

  • Dec 24 2022

    Contribution Submission Deadline

  • Apr 13 2023

    Registration deadline

Sponsored By
Harbin Insititute of Technology
China Instrument and Control Society
Heilongjiang Instrument and Control Society
Chinese Institute of Electronics
IEEE I&M Society Harbin Chapter
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