Deep learning on the production line: A novel lightweight CNN model approach for efficient and fast defect detection

Hakan Tatar; Muhammed Furkan Küçük

Makale detayları

Deep learning on the production line: A novel lightweight CNN model approach for efficient and fast defect detection

Yazarlar:

Hakan Tatar | Muhammed Furkan Küçük

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Enerjisi Üretimi (Yenilenebilir Kaynaklar Dahil | Fotovoltaikler Hariç)

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.14 Issue 2, pp.649-658

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001345098

pISSN:

2564-6605

Anahtar kelimeler:

Özet:

 This paper presents an optimized lightweight CNN model developed using a unique dataset introduced here for the first time to detect defects in manufacturing processes in a factory. The model performance was analyzed comparatively with widely used large-scale deep learning architectures such as VGG16 and ResNet50. All models were trained on the same original dataset, followed by the same approach in tuning hyperparameters such as learning rate, optimization algorithm, and data augmentation strategies. Performance analyses were conducted using fundamental metrics such as accuracy, precision, and F1 score, along with confusion matrices and randomly selected test images. Our proposed model attained high accuracy while reducing computational cost and significantly shortening training time compared to traditional architectures. The results demonstrate that the proposed CNN model achieves a competitive level of accuracy comparable to large-scale deep learning models while serving as a more suitable alternative for low-power hardware systems.

 This paper presents an optimized lightweight CNN model developed using a unique dataset introduced here for the first time to detect defects in manufacturing processes in a factory. The model performance was analyzed comparatively with widely used large-scale deep learning architectures such as VGG16 and ResNet50. All models were trained on the same original dataset, followed by the same approach in tuning hyperparameters such as learning rate, optimization algorithm, and data augmentation strategies. Performance analyses were conducted using fundamental metrics such as accuracy, precision, and F1 score, along with confusion matrices and randomly selected test images. Our proposed model attained high accuracy while reducing computational cost and significantly shortening training time compared to traditional architectures. The results demonstrate that the proposed CNN model achieves a competitive level of accuracy comparable to large-scale deep learning models while serving as a more suitable alternative for low-power hardware systems.

 This paper presents an optimized lightweight CNN model developed using a unique dataset introduced here for the first time to detect defects in manufacturing processes in a factory. The model performance was analyzed comparatively with widely used large-scale deep learning architectures such as VGG16 and ResNet50. All models were trained on the same original dataset, followed by the same approach in tuning hyperparameters such as learning rate, optimization algorithm, and data augmentation strategies. Performance analyses were conducted using fundamental metrics such as accuracy, precision, and F1 score, along with confusion matrices and randomly selected test images. Our proposed model attained high accuracy while reducing computational cost and significantly shortening training time compared to traditional architectures. The results demonstrate that the proposed CNN model achieves a competitive level of accuracy comparable to large-scale deep learning models while serving as a more suitable alternative for low-power hardware systems.

 Bu makale, bir fabrikadaki üretim süreçlerinde ortaya çıkan kusurların tespiti için burada ilk kez sunulan benzersiz bir veri kümesi kullanılarak geliştirilen, optimize edilmiş hafif bir CNN modelini tanıtmaktadır. Model performansı, VGG16 ve ResNet50 gibi yaygın kullanılan büyük ölçekli derin öğrenme mimarileriyle karşılaştırmalı olarak analiz edilmiştir. Tüm modeller, aynı özgün veri kümesi üzerinde, öğrenme oranı, optimizasyon algoritması ve veri artırma stratejileri gibi sabit hiperparametrelerle eğitilmiştir. Performans analizleri doğruluk, kesinlik ve F1 skoru gibi temel metriklerin yanı sıra, karmaşıklık matrisleri ve rastgele test görüntüleri üzerinden gerçekleştirilmiştir. Önerdiğimiz model, geleneksel mimarilere kıyasla daha düşük hesaplama maliyeti ve çok daha kısa eğitim süresi ile yüksek doğruluk elde etmiştir. Elde edilen sonuçlar, önerilen CNN modelinin büyük ölçekli derin öğrenme modelleriyle rekabet edebilecek düzeyde doğruluk sunarken, düşük güçlü donanıma sahip sistemler için daha uygun bir alternatif olduğunu göstermektedir.

Makale detayları

Deep learning on the production line: A novel lightweight CNN model approach for efficient and fast defect detection

Yazarlar:

Hakan Tatar | Muhammed Furkan Küçük

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Enerjisi Üretimi (Yenilenebilir Kaynaklar Dahil | Fotovoltaikler Hariç)

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.14 Issue 2, pp.649-658

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001345098

pISSN:

2564-6605

CNN

Fault Detection

Solar Panel

By-Pass Diode

Classification MethodsCNN

Hata Tespiti

Güneş Paneli

Baypas Diyotu

Sınıflandırma Metotları

Sayı

Makale detayları

Yazarlar: Hakan Tatar | Muhammed Furkan Küçük

Yayıncı: Nigde Omer Halisdemir University

Ana Başlıklar: Elektrik Enerjisi Üretimi (Yenilenebilir Kaynaklar Dahil | Fotovoltaikler Hariç)

Dergi: Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.14 Issue 2, pp.649-658

Belge Tipi: Makale

Yayın Tarihi: 4/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001345098

pISSN: 2564-6605

Sayı

Yazarlar:

Hakan Tatar | Muhammed Furkan Küçük

Yayıncı:

Nigde Omer Halisdemir University

Ana Başlıklar:

Elektrik Enerjisi Üretimi (Yenilenebilir Kaynaklar Dahil | Fotovoltaikler Hariç)

Dergi:

Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi Vol.14 Issue 2, pp.649-658

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001345098

pISSN:

2564-6605