Printed Circuit Board Defect Detection Based on Lightweight Deep Learning Fusion Model.

Yuling Wang, Zhicheng Chen, Jie Wang, Peng Shang, Arcot Sowmya, Changming Sun

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Defect-Intent Ambiguity Addressing for Training-Free Deterministic PCB Defect Localization via Template Selection and Dissimilarity Mapping.Sensors (Basel, Switzerland) · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yuling WangSchool of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China.

Zhicheng ChenSchool of Software, East China University of Technology, Nanchang 330013, China.

Jie WangSchool of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China.

Peng ShangSchool of Artificial Intelligence and Information Engineering, East China University of Technology, Nanchang 330013, China.

Arcot SowmyaSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Changming SunCSIRO Data61, Sydney, NSW 1710, Australia.ORCID 0000-0001-5943-1989

Funding

National Natural Science Foundation of China No. 62066003National Scholarship Council of China No. 202208360143

6 · The paper itself

Abstract

Printed circuit boards (PCBs) are ubiquitous and essential electronic components. Tiny targets and high precision are the focus of PCB defect detection. This paper proposes an improved model focusing on tiny defect detection and model compression to achieve better performance in PCB defect detection. The improved model has a compact structure based on MobileNet v3 Small-CA and an image-cutting layer. Moreover, it applies an improved multi-scale fusion step with a location weighted mechanism to enhance representation performance. The proposed model outperforms state-of-the-art detection algorithms such as Faster R-CNN, EfficientDet, SSD, and YOLO v7, based on experimental results on a public synthetic PCB dataset. The proposed tiny object detection model has better performances on speed and detection accuracy, thereby benefitting the manufacturing industries associated with PCBs.

Indexed as

defect detectionfeature fusionlightweight deep learning modelPCB

Identifiers

PMID41471399

DOI10.3390/s25247403

PMCPMC12736545

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.