ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection

Xiem HoangVan, Dang Bui Dinh, Thanh Nguyen Canh, Van-Truong Nguyen
6/16/2025
cs.CVcs.AIeess.IV

No official code found. Best implementation: dangdinh17/ESRPCB (1 star, last commit 2025-08-09).

Abstract

Printed Circuit Boards (PCBs) are critical components in modern electronics, which require stringent quality control to ensure proper functionality. However, the detection of defects in small-scale PCBs images poses significant challenges as a result of the low resolution of the captured images, leading to potential confusion between defects and noise. To overcome these challenges, this paper proposes a novel framework, named ESRPCB (edgeguided super-resolution for PCBs defect detection), which combines edgeguided super-resolution with ensemble learning to enhance PCBs defect detection. The framework leverages the edge information to guide the EDSR (Enhanced Deep Super-Resolution) model with a novel ResCat (Residual Concatenation) structure, enabling it to reconstruct high-resolution images from small PCBs inputs. By incorporating edge features, the super-resolution process preserves critical structural details, ensuring that tiny defects remain distinguishable in the enhanced image. Following this, a multi-modal defect detection model employs ensemble learning to analyze the super-resolved

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Code Implementations(1)

dangdinh17/ESRPCBCommunity57%

[EAAI] Official Pytorch Implementation for "ESRPCB: An edge guided super-Resolution model and ensemble learning for tiny Printed Circuit Board defect detection"

10Python, Jupyter NotebookMar 31, 20251 years ago

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Cite this paper

@article{hoangvan2025esrpcb,
  title  = {ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection},
  author = {Xiem HoangVan and Dang Bui Dinh and Thanh Nguyen Canh and Van-Truong Nguyen},
  year   = {2025},
  eprint = {2506.13476},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.13476v1}
}

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