The PCB Surface Defect Detection System Based on GPU Acceleration

2015 ◽  
Vol 713-715 ◽  
pp. 347-352
Author(s):  
Yu Liu ◽  
Si Zhe Li
Keyword(s):  
Defect Detection ◽  
Rapid Detection ◽  
Surface Defect ◽  
Printed Circuit Board ◽  
Detection System ◽  
Processing System ◽  
Circuit Board ◽  
Computing Platform ◽  
Surface Defect Detection ◽  
Time Requirements

Defective products are unavoidable in printed circuit board production process, so rapid detection and identificationmethods are badly in need of. PCB surface defect detection including a series of processing such as surface imagecapture, mixed noise filtering,images registering and so on, so it takes a lot of CPU time. To improve detection speed, based on GPU parallel computing platform, we designed a reasonable parallel processing system for PCB defect detectionto meet the need of real-time requirements of a production line. Experimental results show that parallel image processing algorithms based on GPU can achieve good results compared to the CPU-based serial algorithm (with speed up ratio up to8.34 in this paper),providing a new approachfor rapid detection of PCB surface defect.

scholarly journals YOLOv4-MN3 for PCB Surface Defect Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11701
Author(s):  
Xinting Liao ◽  
Shengping Lv ◽  
Denghui Li ◽  
Yong Luo ◽  
Zichun Zhu ◽  
...  
Keyword(s):  
Defect Detection ◽  
High Performance ◽  
Surface Defect ◽  
Printed Circuit Board ◽  
Surface Defects ◽  
Activation Function ◽  
Production Quality ◽  
Circuit Board ◽  
Detection Accuracy ◽  
Surface Defect Detection

Surface defect detection for printed circuit board (PCB) is indispensable for managing PCB production quality. However, automatic detection of PCB surface defects is still a challenging task because, even within the same category of surface defect, defects present great differences in morphology and pattern. Although many computer vision-based detectors have been established to handle these problems, current detectors struggle to achieve high detection accuracy, fast detection speed and low memory consumption simultaneously. To address those issues, we propose a cost-effective deep learning (DL)-based detector based on the cutting-edge YOLOv4 to detect PCB surface defect quickly and efficiently. The YOLOv4 is improved upon with respect to its backbone network and the activation function in its neck/prediction network. The improved YOLOv4 is evaluated with a customized dataset, collected from a PCB factory. The experimental results show that the improved detector achieved a high performance, scoring 98.64% on mean average precision (mAP) at 56.98 frames per second (FPS), outperforming the other compared SOTA detectors. Furthermore, the improved YOLOv4 reduced the parameter space of YOLOv4 from 63.96 M to 39.59 M and the number of multiply-accumulate operations (Madds) from 59.75 G to 26.15 G.

Research on Key Technology of Cold-Rolled Aluminum Plate Surface Defect Detection System

2013 ◽  
Vol 433-435 ◽  
pp. 915-918 ◽  
Author(s):  
Hai Lun Zhang ◽  
Xing Guang Qi ◽  
Xiao Ting Li
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Detection System ◽  
Aluminum Plate ◽  
Aluminum Surface ◽  
Image Processing Algorithm ◽  
Key Technologies ◽  
Rolled Aluminum ◽  
Surface Defect Detection ◽  
Cold Rolled

This paper presents the research of several key technologies during the implementation of cold-rolling aluminum surface defect detection system, including the difficulty of achieving these key technologies and the improvement of image processing algorithm. Through the installation and commissioning on actual production line, summarize and analyze the requirements of the hardware and software design for highly reflective aluminum plate, to achieve the control of product quality at present.

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