Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting

Accurate localization and counting of tiny electronic components in high-resolution X-ray images is a critical yet challenging task in nuclear science, radiation imaging, and industrial quality control. Traditional methods suffer from poor generalization in cluttered scenes, while deep learning approaches are limited by the lack of large-scale annotated datasets. This study aims to develop a semi-supervised detection framework that achieves high-precision component localization and counting in 3072 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mo>×</mml:mo> </mml:math> 3072-pixel X-ray images, while significantly reducing manual annotation costs. We propose YOLO-DEP, a novel object detector that integrates the YOLOv11 architecture with a Deep Encoding Processor (DEP) and a Graph Attention Network (GAT). The DEP module enhances feature discrimination for dense and small targets via half-channel and spatial attention mechanisms. A semi-supervised label propagation strategy is designed to generate high-confidence pseudo-labels from only one labeled image per category, leveraging feature similarity graphs and GAT-based confidence filtering. We also introduce LEEC , a large-scale X-ray dataset for electronic component counting, containing 720 images across 49 component types. YOLO-DEP outperforms state-of-the-art detectors on both LEEC and DOTAv1 datasets. Specifically, YOLO-DEP-x achieves 79.2% mAP50 and 70.9% mAP50–95 on LEEC, with a counting error rate as low as 0.8, providing an immediately deployable solution for industrial automation, nuclear electronics inspection, fuel-assembly verification and broader radiation-based quality-control lines.