Skripsi

PENINGKATAN KUALITAS CITRA DENGAN METODE RETINEX UNTUK DETEKSI WAJAH PADA CITRA GELAP

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Penilaian

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Facial images captured under low-light conditions often suffer from visual degradation, including low contrast, inadequate brightness, and loss of important details, which negatively impact face detection accuracy. This study aims to enhance the quality of low-light images using the Retinex method as a preprocessing step and to evaluate its effect on the performance of the YOLOv5 face detection algorithm. A total of 6,000 low-light facial images were used in the experiment. Image enhancement was assessed using brightness, contrast, entropy, Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index Measure (SSIM). The Retinex method successfully improved the average brightness to 104.57, contrast to 21.50, entropy to 391,715, and reduced the MSE to 8,985.14. However, the average PSNR and SSIM values—8.74 dB and 0.1359 respectively—indicate that some distortion remained in several images. The enhanced images were then used to train and test the YOLOv5 model, achieving the best performance at a batch size of 32 and 100 epochs, with a confidence score of 0.93.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005552	T183565	T1835652025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1835652025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 71 hlm.; ilus,; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

621.367 07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Teknik Informatika
Teknologi pencitraan dan sistem penglihatan mesin

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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