Evaluation of Lightweight Object Detection and Instance Segmentation Techniques for Industrial Printed Circuit Board Solder Quality Assessment

Year 2025, Volume: 21 Issue: 4, 128 - 138, 29.12.2025

Tuncay Soylu , Emel Soylu

https://doi.org/10.18466/cbayarfbe.1669378

Abstract

The rapid progress of deep learning has transformed the detection of soldering defects in printed circuit boards (PCBs), outperforming traditional manual inspections and rule-based machine vision systems.This study evaluates the performance of state-of-the-art YOLO models—specifically YOLOv11 and YOLOv12—for both object detection and instance segmentation of solder defects in aerospace PCBs using the open-source SolDef_AI dataset. We compare multiple variants (nano, small, and medium) to assess their accuracy, efficiency, and suitability for industrial quality control. Our experiments reveal that YOLO-v11s-seg achieves the highest mean average precision (mAP50-95: 0.853 for detection, 0.822 for segmentation), demonstrating superior defect localization capabilities, particularly for challenging classes such as "poor_solder" and "spike." While YOLOv12 models exhibit competitive detection performance, they show slightly lower segmentation accuracy, indicating potential areas for architectural refinement. Smaller models (YOLO-v11n, YOLO-v12n) offer a favorable balance between speed and precision, making them viable for real-time applications. The findings highlight the effectiveness of YOLO-based deep learning in automating solder defect inspection, with implications for improving manufacturing quality assurance in electronics production.

Keywords

Image Processing , Object Detection , Quality Control , Soldering Defect Detection

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