IMAGE PROCESSING-BASED QUALITY CONTROL SYSTEM TO DETERMINE THE SURFACE DEFECT IN WOODEN RAW MATERIALS

Yaren Çelik; Berna Dengiz; Selda Güney

Article Details

IMAGE PROCESSING-BASED QUALITY CONTROL SYSTEM TO DETERMINE THE SURFACE DEFECT IN WOODEN RAW MATERIALS

Authors:

Yaren Çelik | Berna Dengiz | Selda Güney

Publisher:

Süleyman Demirel University

Main Topics:

Endüstri Mühendisliği | Elektrik Mühendisliği

Journal:

Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 4, pp.1365-1382

Document Type:

Article

Publication Date:

12/2023

Document Language:

Turkish

Document ID (Org No.):

2001307532

pISSN:

1308-6693

Keywords:

Abstract:

 In modern industries, wood raw materials serve a multitude of purposes across various sectors. Undetected flaws within these materials can significantly impact products, affecting both their visual appeal and durability, leading to compromised marketability. Manual and visual inspection of flawed raw materials is a complex process prone to errors in judgment. The rapid evolution of digital technologies has spurred the creation of automated systems designed for precise quality assessments. This progress enables the early identification of defects during production, thereby preventing the occurrence of defective or substandard end-products. Consequently, this approach reduces labor, material, and time wastage, effectively cutting down associated costs.The present study focuses on distinguishing between defective and intact raw materials using images obtained through a specialized camera system. Deep learning techniques in image processing are employed for this purpose, with a particular emphasis on the Convolutional Neural Network CNN, a classification method chosen for defect detection. A range of CNN architectures, including ShuffleNet, AlexNet, and GoogleNet, were tested, and the most effective one was identified. The results of these experiments demonstrate that within the realm of CNNs, architectures like MobileNet, DenseNet, and Inception have proven highly successful, leading to improved accuracy in quality control systems. Notably, the Inception-V3 architecture emerges as the top performer, achieving a test accuracy of 88.41.

 In modern industries, wood raw materials serve a multitude of purposes across various sectors. Undetected flaws within these materials can significantly impact products, affecting both their visual appeal and durability, leading to compromised marketability. Manual and visual inspection of flawed raw materials is a complex process prone to errors in judgment. The rapid evolution of digital technologies has spurred the creation of automated systems designed for precise quality assessments. This progress enables the early identification of defects during production, thereby preventing the occurrence of defective or substandard end-products. Consequently, this approach reduces labor, material, and time wastage, effectively cutting down associated costs.The present study focuses on distinguishing between defective and intact raw materials using images obtained through a specialized camera system. Deep learning techniques in image processing are employed for this purpose, with a particular emphasis on the Convolutional Neural Network CNN, a classification method chosen for defect detection. A range of CNN architectures, including ShuffleNet, AlexNet, and GoogleNet, were tested, and the most effective one was identified. The results of these experiments demonstrate that within the realm of CNNs, architectures like MobileNet, DenseNet, and Inception have proven highly successful, leading to improved accuracy in quality control systems. Notably, the Inception-V3 architecture emerges as the top performer, achieving a test accuracy of 88.41.

 In modern industries, wood raw materials serve a multitude of purposes across various sectors. Undetected flaws within these materials can significantly impact products, affecting both their visual appeal and durability, leading to compromised marketability. Manual and visual inspection of flawed raw materials is a complex process prone to errors in judgment. The rapid evolution of digital technologies has spurred the creation of automated systems designed for precise quality assessments. This progress enables the early identification of defects during production, thereby preventing the occurrence of defective or substandard end-products. Consequently, this approach reduces labor, material, and time wastage, effectively cutting down associated costs.The present study focuses on distinguishing between defective and intact raw materials using images obtained through a specialized camera system. Deep learning techniques in image processing are employed for this purpose, with a particular emphasis on the Convolutional Neural Network CNN, a classification method chosen for defect detection. A range of CNN architectures, including ShuffleNet, AlexNet, and GoogleNet, were tested, and the most effective one was identified. The results of these experiments demonstrate that within the realm of CNNs, architectures like MobileNet, DenseNet, and Inception have proven highly successful, leading to improved accuracy in quality control systems. Notably, the Inception-V3 architecture emerges as the top performer, achieving a test accuracy of 88.41.

 Günümüzde ahşap ham madde malzemeleri birçok endüstride kullanılmaktadır. Ahşap ham madde üzerinde gözden kaçan kusurlar, son ürünü görsel açıdan ve dayanıklılık bakımından etkileyerek satışını engelleyebilir. Kusurlu ham maddeler üzerinde elle veya görsel kontrol zor ve yanıltıcı olabilir. Sürekli gelişen dijital teknoloji ve akıllı sistemler sayesinde, kalite kontrol için otomasyon sistemleri geliştirilmektedir. Böylece üretimin erken aşamalarında kusurlar tespit edilebilmektedir. Son ürünün kusurlu veya hatalı olması engellenebildiğinde iş gücü, malzeme ve zaman kayıpları önlenebilecek, maliyetler düşürülebilecektir. Bu çalışmada, özel bir kamera sistemi ile elde edilen görüntüler, görüntü işleme temelli Derin Öğrenme DÖ yöntemlerinde kullanılarak ahşap ham maddenin kusurlu olup olmadığı ayrımı yapılmaktadır. Kusurların tespitinde kullanılacak model ayrım odaklı bir yöntem olan Evrişimsel Sinir Ağı ESA ile geliştirilmiş olup tercih edilen bir yöntemdir. Çalışmada, ShuffleNet, AlexNet, GoogleNet gibi ESA mimarileri denenmiş ve en uygun mimari belirlenmiştir. Çalışmalar sonucunda, ESA mimarileri arasında kusurlu ve kusursuz ahşap ürünlerini belirlemek için kullanılan başarılı yöntemler olan MobileNet, DenseNet ve Inception mimarilerinin, kalite kontrol sistemleri için daha yüksek doğruluk oranları ile sonuçlandığı görülmüştür. En iyi sonuç ise, Inception-V3 mimarisi ile elde edilmiştir ve test doğruluğu 88,41 olarak belirlenmiştir.

Article Details

IMAGE PROCESSING-BASED QUALITY CONTROL SYSTEM TO DETERMINE THE SURFACE DEFECT IN WOODEN RAW MATERIALS

Authors:

Yaren Çelik | Berna Dengiz | Selda Güney

Publisher:

Süleyman Demirel University

Main Topics:

Endüstri Mühendisliği | Elektrik Mühendisliği

Journal:

Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 4, pp.1365-1382

Document Type:

Article

Publication Date:

12/2023

Document Language:

Turkish

Document ID (Org No.):

2001307532

pISSN:

1308-6693

Deep Learning

Image Processing

Wood Defect Detection

Convolutional Neural Network

ClassificationDerin Öğrenme

Görüntü İşleme

Ahşap Kusur Tespiti

Evrişimsel Sinir Ağları

Sınıflandırma

Issues

Article Details

Authors: Yaren Çelik | Berna Dengiz | Selda Güney

Publisher: Süleyman Demirel University

Main Topics: Endüstri Mühendisliği | Elektrik Mühendisliği

Journal: Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 4, pp.1365-1382

Document Type: Article

Publication Date: 12/2023

Document Language: Turkish

Document ID (Org No.): 2001307532

pISSN: 1308-6693

Issues

Authors:

Yaren Çelik | Berna Dengiz | Selda Güney

Publisher:

Süleyman Demirel University

Main Topics:

Endüstri Mühendisliği | Elektrik Mühendisliği

Journal:

Mühendislik Bilimleri ve Tasarım Dergisi Vol.11 Issue 4, pp.1365-1382

Document Type:

Article

Publication Date:

12/2023

Document Language:

Turkish

Document ID (Org No.):

2001307532

pISSN:

1308-6693