Occupancy measurement in under actuated zones : yolo based deep learning approach

Pencarian berdasarkan :

SEMUA Pengarang Subjek ISBN/ISSN Pencarian Spesifik

Pencarian terakhir:

Image of Occupancy measurement in under actuated zones : yolo based deep learning approach

Text

Occupancy measurement in under actuated zones : yolo based deep learning approach

Mohammad Faiz Azhary - Nama Orang - Pengarang Utama; Yaddarabullah - Nama Orang - Pembimbing; Ade Syahputra - Nama Orang - Pembimbing;

The challenge of accurately detecting and identifying individuals within under-actuated zones presents a relevant research problem in occupant detection. This study aims to address the challenge of occupant detection in under-actuated zones through the utilization of the You Only Look Once version 8 (YOLO v8) object detection model. The research methodology involves a comprehensive evaluation of YOLO v8's performance across three distinct zones, where its precision, accuracy, and recall capabilities in identifying occupants are rigorously assessed. The outcomes of this performance evaluation, expressed through quantitative metrics, provide compelling evidence of the efficacy of the YOLO v8 model in the context of occupant detection in under-actuated zones. Across these three diverse under-actuated zones, YOLO v8 consistently exhibits remarkable mean Average Precision (mAP) scores, achieving 99.2% in Zone 1, 78.3% in Zone 2, and 96.2% in Zone 3. These mAP scores serve as a testament to the model's precision, indicating its proficiency in accurately localizing and identifying occupants within each zone. Furthermore, YOLO v8 demonstrates impressive efficiency in executing occupant detection tasks. The model boasts rapid processing times, with all three zones being analyzed in a matter of milliseconds. Specifically, YOLO v8 achieves execution times of 0.004 seconds in both Zone 1 and Zone 3, while Zone 2, which entails slightly more computational effort, still maintains an efficient execution time of 0.024 seconds. This efficiency constitutes a pivotal advantage of YOLO v8, as it ensures expeditious and effective occupant detection in the context of under-actuated zones.

Ketersediaan

TI24/008 TI 24/008 Prodi Teknik Informatika (Ruang Skripsi dan Tesis) Tersedia

Informasi Detail

Judul Seri: -
No. Panggil: TI 24/008
Penerbit: Jakarta : Universitas Trilogi., 2024
Deskripsi Fisik: -
Bahasa: Indonesia
ISBN/ISSN: -
Klasifikasi: TI
Tipe Isi: text
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: Information technology
Trilogi University--Information technology
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Mohammad Faiz Azhary

Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas

Tidak Ada Data

Komentar

Anda harus masuk sebelum memberikan komentar