ArticleScientific reports2023
Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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.
Who cites it
16 citing papers in PubMed.
- EP-YOLO: A Printed Circuit Board Defect Detection Network Integrating Coordinate Attention and Multi-Level Gradient Flow Optimization.Sensors (Basel, Switzerland) · 2026Article
- FMW-YOLO: A Frequency-Enhanced and Multi-Scale Context-Aware Framework for PCB Defect Detection.Micromachines · 2026Article
- SCMEO-DETR provides a lightweight and high efficiency solution for PCB defect detection based on RT-DETR.Scientific reports · 2026Article
- A Novel PCB Surface Defect Detection Method Based on the GBE-YOLOv8 Model.Micromachines · 2026Article
- Enhanced YOLOv11 framework for high precision defect detection in printed circuit boards.Scientific reports · 2025Article
- Article
- Small defect detection in printed circuit boards based on the multiscale edge strengthening and an improved YOLOv10.Scientific reports · 2025Article
- DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion.Scientific reports · 2025Article
- EcoDetect-YOLOv2: A High-Performance Model for Multi-Scale Waste Detection in Complex Surveillance Environments.Sensors (Basel, Switzerland) · 2025Article
- A novel PCB surface defect detection method based on separated global context attention to guide residual context aggregation.Scientific reports · 2025Article
- SCP-DETR: A efficient small-object-enhanced feature pyramid approach for PCB defect detection.PloS one · 2025Article
- An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information.Sensors (Basel, Switzerland) · 2024Article
- Enhanced photovoltaic panel defect detection via adaptive complementary fusion in YOLO-ACF.Scientific reports · 2024Article
- Local and Global Context-Enhanced Lightweight CenterNet for PCB Surface Defect Detection.Sensors (Basel, Switzerland) · 2024Article
- EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.Sensors (Basel, Switzerland) · 2024Article
- Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
To solve the problem of missed and false detection caused by the large number of tiny targets and complex background textures in a printed circuit board (PCB), we propose a global contextual attention augmented YOLO model with ConvMixer prediction heads (GCC-YOLO). In this study, we apply a high-resolution feature layer (P2) to gain more details and positional information of small targets. Moreover, in order to suppress the background noisy information and further enhance the feature extraction capability, a global contextual attention module (GC) is introduced in the backbone network and combined with a C3 module. Furthermore, in order to reduce the loss of shallow feature information due to the deepening of network layers, a bi-directional weighted feature pyramid (BiFPN) feature fusion structure is introduced. Finally, a ConvMixer module is introduced and combined with the C3 module to create a new prediction head, which improves the small target detection capability of the model while reducing the parameters. Test results on the PCB dataset show that GCC-YOLO improved the Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 0.2%, 1.8%, 0.5%, and 8.3%, respectively, compared to YOLOv5s; moreover, it has a smaller model volume and faster reasoning speed compared to other algorithms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.