Skripsi

OPTIMASI DETEKSI WAJAH MENGGUNAKAN SUPER RESOLUTION DAN YOLO-SAHI

---

Penilaian

---

Face detection in low-resolution images remains a significant challenge in computer vision applications, particularly for surveillance systems and crowd analysis. This study proposes an integrated approach combining Enhanced Super-Resolution GAN (ESRGAN) for image quality enhancement and YOLO-SAHI (You Only Look Once with Slicing Aided Hyper Inference) for improved detection of small faces. The research utilizes the WIDER FACE dataset with varying difficulty levels to evaluate performance across different scenarios. Experimental results demonstrate that the hybrid approach significantly improves detection accuracy, with SAHI integration increasing recall by 35% for small faces compared to standalone YOLOv8. The complete YOLO-SAHI-ESRGAN system achieves 78.2% mean Average Precision (mAP) while maintaining reasonable processing latency of 420 ms per image. ESRGAN effectively enhances image quality, achieving PSNR of 24.43 dB and SSIM of 0.76 for easy cases. The study also develops a practical web-based implementation for real-world application.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005919	T184647	T1846472025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1846472025

Publisher

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

Collation

xv, 101 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

-

Subject(s)

Optimasi Deteksi Wajah

Specific Detail Info

-

Statement of Responsibility

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment