ArticleScientific reports2022
End-to-end deep learning framework for printed circuit board manufacturing defect classification.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- A Hardware-Software Integrated PCB Image Registration Method Based on Local Adaptive KNN and SIFT.Sensors (Basel, Switzerland) · 2026Article
- Optimized recycling of e-waste and tire rubber into sustainable polyurethane composites with enhanced compressive performance.Scientific reports · 2026Article
- FMW-YOLO: A Frequency-Enhanced and Multi-Scale Context-Aware Framework for PCB Defect Detection.Micromachines · 2026Article
- Defect-Intent Ambiguity Addressing for Training-Free Deterministic PCB Defect Localization via Template Selection and Dissimilarity Mapping.Sensors (Basel, Switzerland) · 2026Article
- PCB-defect: An annotated dataset for surface defect detection in printed circuit boards.Data in brief · 2026Article
- MDEB-YOLO: A Lightweight Multi-Scale Attention Network for Micro-Defect Detection on Printed Circuit Boards.Micromachines · 2026Article
- Printed Circuit Board Defect Detection Based on Lightweight Deep Learning Fusion Model.Sensors (Basel, Switzerland) · 2025Article
- An improved EAE-DETR model for defect detection of server motherboard.Scientific reports · 2025Article
- ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification.Sensors (Basel, Switzerland) · 2025Article
- Organic Synaptic Transistors and Printed Circuit Board Defect Inspection with Photonic Stimulation: A Novel Approach Using Oblique Angle Deposition.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- YOLO-SUMAS: Improved Printed Circuit Board Defect Detection and Identification Research Based on YOLOv8.Micromachines · 2025Article
- Defect detection of printed circuit board assembly based on YOLOv5.Scientific reports · 2024Article
- Local and Global Context-Enhanced Lightweight CenterNet for PCB Surface Defect Detection.Sensors (Basel, Switzerland) · 2024Article
- Improved YOLOv8-Based Target Precision Detection Algorithm for Train Wheel Tread Defects.Sensors (Basel, Switzerland) · 2024Article
- Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection.Scientific reports · 2023Article
- Automatic Detection, Classification, and Grading of Lumbar Intervertebral Disc Degeneration Using an Artificial Neural Network Model.Diagnostics (Basel, Switzerland) · 2023Article
- A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler.Sensors (Basel, Switzerland) · 2022Article
- A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.
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