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Dergiler / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2025 / Cilt: 21 Sayı: 4

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

Tuncay Soylu(SAMSUN ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-5541-2219
Emel Soylu(SAMSUN ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, YAZILIM MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-2774-9778
Sayfa
128–138
Yayın Tarihi
2025-12-29
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Image Processing · Object Detection · Quality Control

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.

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Bu Makaleye Atıf Yap

Soylu, T., Soylu, E. (2025). Evaluation of Lightweight Object Detection and Instance Segmentation Techniques for Industrial Printed Circuit Board Solder Quality Assessment. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 21 Sayı: 4, 128–138. https://doi.org/10.18466/cbayarfbe.1669378

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