LUMINANCE ESTIMATION FROM SURFACES WITH DIFFERENT COLOR TEMPERATURE AND LAMP ILLUMINATION ANGLE: A DEEP LEARNING-BASED APPROACH

Birkan Büyükarıkan; Erkan Ülker

doi:10.17780/ksujes.1626795

TR EN

FARKLI RENK SICAKLIĞI VE AYDINLATMA AÇISI İLE YÜZEYLERDEN PARILTI TAHMİNİ: DERİN ÖĞRENME TABANLI BİR YAKLAŞIM

Öz

Geleneksel yöntemlerle parıltı tahmini, elektronik sistemler yardımıyla gerçekleştirilmektedir. Ancak, nesne üzerindeki parıltıyı etkileyen renk sıcaklığı ve lambanın nesneyi aydınlatma açısı gibi aydınlatma özelliklerinin değişimi geleneksel yöntemlerle parıltı tahminini zorlaştırmaktadır. Dolayısıyla bu çalışmada, parıltı tahminine alternatif bir çözüm sunmak amacıyla evrişimsel sinir ağları (CNN) modellerinden yararlanılarak görüntü tabanlı bir yaklaşım önerilmiştir. Çalışmada, renk sıcaklığı ve lambanın konum açısının etkileri göz önünde bulundurularak, kusurlu ve kusursuz elma görüntüleri üzerinde parıltı tahmini yapılmıştır. Elde edilen sonuçlara göre, GoogLeNet modeli, öğrenme oranının 0.001 ve yığın boyutunun 8 olduğu değerlerde en iyi performansı sergileyerek, daha düşük Karekök Ortalama Kare Hata (RMSE) değeri ile en iyi parıltı tahminini gerçekleştirmiştir. Renk sıcaklıklarına göre, kusurlu elmalar en düşük RMSE değerini 2700 K renk sıcaklığında ve kusursuz elmalar ise en düşük RMSE değerini 6500 K renk sıcaklığında göstermiştir. Renk sıcaklıklarına göre; en iyi parıltı tahmini, 6500 K renk sıcaklığında 5.023 cd/m² RMSE değerindedir. Lamba açısına göre; kusurlu elmalar en düşük RMSE değerini 60 derecelik açıda 5.106 cd/m² ve kusursuz elmalar en düşük RMSE değeri ise 45 derecelik açıda 6.411 cd/m² olarak elde etmiştir.

Anahtar Kelimeler

LUMINANCE ESTIMATION FROM SURFACES WITH DIFFERENT COLOR TEMPERATURE AND LAMP ILLUMINATION ANGLE: A DEEP LEARNING-BASED APPROACH

Abstract

Traditional methods of luminance estimation are performed with the help of electronic systems. However, the changes in lighting properties, such as color temperature and the lamp illumination angle, affect the luminance on the object, making luminance estimation difficult compared to traditional methods. Therefore, this study proposes an image-based approach using convolutional neural networks (CNNs) models to provide an alternative solution for luminance estimation. In this study, luminance estimation is performed on defective and healthy apple images by considering the effects of color temperature and lamp illumination angle. According to the results, the GoogLeNet model exhibited the best performance at values where the learning rate was 0.001 and the batch size was eight. It also performed the best luminance estimation with a lower Root Mean Square Error (RMSE) value. According to color temperatures, defective apples showed the lowest RMSE value at warm white light, and healthy apples showed the lowest RMSE value at cold white light. According to color temperatures, the best luminance estimation is a 5.023 cd/m² RMSE value at a cold white light. According to lamp angle, defective apples obtained the lowest RMSE value at 5.106 cd/m² at a 60-degree angle, and healthy apples obtained the lowest RMSE value at 6.411 cd/m² at a 45-degree angle.

Keywords

Teşekkür

This work was supported by the Scientific Research Project at Konya Technical University, Konya, Turkey (No. 201113006).

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Görüşü , Görüntü İşleme , Makine Öğrenme (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Birkan Büyükarıkan ^*
0000-0002-9703-9678
Türkiye

Erkan Ülker
0000-0003-4393-9870
Türkiye

Yayımlanma Tarihi

3 Haziran 2025

Gönderilme Tarihi

25 Ocak 2025

Kabul Tarihi

22 Mart 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 2

DOI

https://doi.org/10.17780/ksujes.1626795

IZ

https://izlik.org/JA48DY32BH

Kaynak Göster

RIS / Bibtex

APA

Büyükarıkan, B., & Ülker, E. (2025). LUMINANCE ESTIMATION FROM SURFACES WITH DIFFERENT COLOR TEMPERATURE AND LAMP ILLUMINATION ANGLE: A DEEP LEARNING-BASED APPROACH. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 883-896. https://doi.org/10.17780/ksujes.1626795