TL;DR
The proposed Multi-Scale Feature Fusion with Mixed Supervision Network (MSFFMS-Net), a novel architecture that enhances defect detection accuracy while minimizing annotation effort, achieves competitive performance under mixed supervision.
Automated bridge surface defect detection is essential for improving the efficiency and objectivity of infrastructure inspection under complex field imaging conditions. This study proposes a Multi-Scale Detection Transformer (MS-DETR), an RT-DETR-based detector that integrates HeteroFusionNet, a multi-objective scale-a...
Steel surface defect detection is vital for guaranteeing product quality in contemporary manufacturing. However, traditional steel surface defect detection algorithms often face challenges due to insufficient resilience in feature extraction under complex backgrounds. To address this, we present a framework for def...
Industrial surface defect detection is core to intelligent manufacturing quality assurance, guaranteeing stable product quality and reliable production. Conventional defect detection deep learning algorithms relying on Red-Green-Blue (RGB) images suffer low accuracy and weak robustness amid complicated industrial scene...
In industrial quality control, deep learning-based weld defect detection has shown promise but is often hindered by the need for extensive labeled datasets. This study introduces a novel graph neural network (GNN) approach for few-shot classification of weld defect images, aimed at mitigating data dependence and enabli...
A prototype-enhanced YOLO-based defect detection model for few-shot industrial applications that outperforms mainstream defect detection models, validating its effectiveness in few-shot industrial defect detection tasks.
CMNet, a novel hybrid CNN–Mamba detection network with a dual-branch, heterogeneous feature-extraction architecture, is proposed, which significantly outperforms state-of-the-art methods on fabric defect detection datasets.
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