.png){kind=icon}
返回
Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models
.png){kind=icon}
PRE
AI
DOI：10.1109/JAS.2024.124422.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automated optical inspection (AOI) is a significant process in printed circuit board assembly (PCBA) production lines which aims to detect tiny defects in PCBAs. Existing AOI equipment has several deficiencies including low throughput, large computation cost, high latency, and poor flexibility, which limits the efficiency of online PCBA inspection. In this paper, a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed. In this method, the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection framework. To improve the performance of the model, extensive real PCBA images are collected from production lines as datasets. Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices. Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods. Our method can be integrated into a lightweight inference system and promote the flexibility of AOI. The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.
Keyword:
Computational modeling
Semantic segmentation
Printed circuits
Neural networks
Optimization methods
Production
Throughput
Automated optical inspection (AOI)
deep learning
defect detection
printed circuit board assembly (PCBA)
semantic segmentation
期刊
I
IF:
19.2
论文数:
1.4K
被引数:
1.1W
机构
引用论文
Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Differential Effects of Lovastatin on Cisplatin Responses in Normal Human Mesothelial Cells versus Cancer Cells: Implication for TherapyPLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network基于改进的Faster-RCNN和特征金字塔网络的PCB表面缺陷检测IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容