YOLOv8 tabanlı PCB Hata Tespit ve Sınıflandırma Sistemi

Abstract

Baskı Devre Kartlarının yüzey muayenesi, üretim sırasında oluşabilecek küçük hataların bile ciddi maliyetlere yol açması nedeniyle en önemli kalite kontrol süreçlerinden biridir. Bu çalışmada baskı devrelerde (BD) sık karşılaşılan altı hatanın tespiti ve sınıflandırılması için YOLOv8 tabanlı bir sistem geliştirilmiştir. Doğruluk, hız ve aynı anda birden fazla hatayı tespit edebilme yeteneği nedeni ile önerilen yöntem, diğer BD hata tespit yöntemlerine kıyasla, üretim bantlarında kullanıma daha uygundur. Sistem aynı zamanda hedefe yönelik denetim için özelleştirilebilir hata seçim olanağı da sunmaktadır. Denemelerde %99,2'lik ortalama hassasiyet değerine ulaşılmıştır. Yüksek doğruluk, hızlı işlem yapısı, kararlılık ve kullanıcı dostu ara yüzün birleşimi, önerilen sistemin endüstriyel uygulamalar için umut verici bir aday olabileceğini ve sistemin gerçek dünyadaki BD üretim ortamlarında kullanıma uygunluğunu göstermektedir.

Keywords

• PCB Hata Tespiti
• YOLO
• Sınıflandırma

YOLOv8-based PCB Defect Detection and Classification System

Abstract

Surface inspection of Printed Circuit Boards (PCB) is one of the most crucial quality control processes due to potential serious costs of even small errors occurred during production. In this study, a YOLOv8 based system is developed for detection and classification of six common errors occurs on PCBs. In terms of accuracy, speed, and the ability to detect multiple defects simultaneously, proposed method is more suitable for use in production compared to other PCB defect detection methods. Proposed system also offers customizable defect selection for targeted inspection. Experimental results show an impressive mean average precision of 99.2%. Combination of high accuracy, fast processing speed, stability, and user-friendly interface makes it a promising candidate for industrial applications demonstrate the system's suitability for real-world PCB manufacturing environments.

Keywords

• PCB Defect Detection
• YOLO
• Classification

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