Baskı Devre Kartı Kusur Tespiti için YOLOv10 ve YOLOv11 Mimarilerinin Karşılaştırmalı Analizi

Abstract

Baskı Devre Kartı (Printed Circuit Board-PCB) kusurlarının tespiti, üretim verimliliği ve kalite kontrol açısından kritik öneme sahiptir. Bu çalışmada, PCB kusur tespiti için derin öğrenme tabanlı nesne algılama mimarileri YOLOv10 ve YOLOv11 karşılaştırılmıştır. Analizlerde her iki mimarinin Nano, Small ve Medium varyasyonları kullanılmış, performansları SGD ve Adamax optimizasyon algoritmalarıyla değerlendirilmiştir. Deneyler, altı kusur kategorisi içeren ve 10.668 görüntüden oluşan PKU-Market-PCB veri kümesi üzerinde gerçekleştirilmiştir. Sonuçlar, SGD ile eğitilen YOLOv10-Medium modelinin %70.30 mAP@50:95 değeriyle en yüksek doğruluğu sağladığını göstermiştir. Ancak bu model daha yüksek hesaplama maliyetine sahiptir. Doğruluk ve hesaplama verimliliği birlikte değerlendirildiğinde, Small ölçekli modellerin endüstriyel uygulamalar için daha dengeli bir çözüm sunduğu belirlenmiştir. Ayrıca YOLOv10’un YOLOv11’e göre ortalama %1.53, SGD’nin ise Adamax’a göre ortalama %5.33 daha yüksek performans sağladığı tespit edilmiştir. Gerçekleştirilen deneyler doğrultusunda, PCB kusur tespiti için uygun model-optimizasyon kombinasyonunun belirlenmesini amaçlamaktadır.

Keywords

• Baskı Devre Kartı
• Derin Öğrenme
• Kusur Tespiti
• YOLOv10
• YOLOv11

Comparative Analysis of YOLOv10 and YOLOv11 Architectures for Printed Circuit Board Defect Detection

Abstract

Detecting defects in Printed Circuit Boards (PCBs) is critical for production efficiency and quality control. This study compares the deep learning-based object detection architectures YOLOv10 and YOLOv11 for PCB defect detection. Nano, Small, and Medium variations of both architectures were used in the analyses, and their performance was evaluated using SGD and Adamax optimization algorithms. Experiments were conducted on a PKU-Market-PCB dataset containing six defect categories and 10,668 images. The results showed that the YOLOv10-Medium model trained with SGD achieved the highest accuracy with a value of 70.30 mAP@50:95. However, this model has a higher computational cost. When accuracy and computational efficiency are considered together, it was determined that Small-scale models offer a more balanced solution for industrial applications. Furthermore, it was found that YOLOv10 provided an average of 1.53% higher performance than YOLOv11, and SGD provided an average of 5.33% higher performance than Adamax. The aim of these experiments is to determine the appropriate model-optimization combination for PCB defect detection.

Keywords

• Deep Learning
• Defect Detection
• Printed Circuit Board
• YOLOv10
• YOLO11

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