[Poster Presentation]Research on PCB Small Target Defect Detection Based on Improved YOLOv5 - International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2022)

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：198 Poster Presentation

Start Time：Pending （Asia/Shanghai）

Duration：Pending

Session：[No Session] » [No Session Block]

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 Dates

~~15^th August 2022~~ 25^th September 2022 - Manuscript Submission
15^th October 2022 - Acceptance Notification
1^st November 2022 - Camera Ready Submission
~~1^st November~~ ~~2022~~ 10^th November 2022 - Early Bird Registration

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