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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Soft Computing Exploration
Ramadhani, Uri Arta
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Journal of Soft Computing Exploration

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Improved human image density detection with comparison of YOLOv8 depth level architecture and drop-out implementation Yulita, Winda; Ramadhani, Uri Arta; Mufidah, Zunanik; Atmajaya, Gde KM; Bagaskara, Radhinka; Kesuma, Rahman Indra; Aprilianda, Mohamad Meazza
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.556

Abstract

Energy inefficiency due to Air Conditioners (AC) running in empty rooms contribute to unnecessary energy consumption and increased CO₂ emissions. This study explores how different depth levels of the YOLOv8 architecture and dropout regularization can enhance human density detection for smarter AC control systems. By evaluating model accuracy through Mean Average Precision (mAP50-95), we provide quantitative insights into how these modifications improve detection performance. Our dataset consists of 1363 images taken in an office environment at ITERA under varying lighting conditions and different human presence densities. The results show that the YOLOv8m model performs best, achieving an mAP50-95 score of 0.814 in training and 0.813 in validation, outperforming other YOLOv8 variants. Furthermore, applying dropout regularization improves model generalization, increasing mAP50-95 from 0.552 to 0.6 and effectively reducing overfitting. This study highlights the balance between architectural depth and dropout regularization in YOLOv8, demonstrating its effectiveness in energy-efficient smart buildings. The findings support the potential of deep learning-based human density detection in improving energy conservation strategies, making it a valuable solution for intelligent automation systems.
Co-Authors Achmad Munir Aprilianda, Mohamad Meazza Atmajaya, Gde KM Aulia, Muhammad Athallah Cahya Bagaskara, Radhinka Dean Corio Mufidah, Zunanik Nia Saputri Utami Prasetyawan, Purwono Rahman Indra Kesuma Rheyuniarto Sahlendar Asthan Ridwan, Naufal Taufiq ULFAH, MIA MARIA Yulita, Winda