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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat)
Edita Rosana Widasari, Edita Rosana
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health Social Sciences

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Journal : International Journal of Advances in Intelligent Informatics

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Augmented haar cascade classifier for real-time ball detection in humanoid robots under dynamic environments Setyawan, Gembong Edhi; Widasari, Edita Rosana; Prasetio, Barlian Henryranu; Umar, Yasa Palaguna; Adipratama, Ivan Rafli
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2146

Abstract

This study proposes an Augmented Haar Cascade Classifier (AHCC) to enhance real-time ball detection for humanoid robots operating in dynamic environments. The method integrates Convex Hull mapping, HSV-based segmentation, and Hough Circle validation to overcome challenges such as fluctuating illumination, complex backgrounds, and partial occlusions. Experiments were conducted entirely on a CPU-only Intel NUC platform running ROS without GPU acceleration, using a dataset containing variations in lighting, orientation, scale, and background clutter. Compared with baseline models (standard Haar Cascade Classifier (HCC) and YOLOv5) the proposed AHCC achieved 97% accuracy, 83% recall, 97% precision, and an 89% F1-score, while requiring only 0.00849 s per frame with 8.97% memory usage. Although YOLOv5 reached 99% accuracy, it demanded higher computational resources (0.0344 s per frame, 22.3% memory usage), limiting its practicality for embedded robotic systems. The AHCC therefore offers an optimal balance between detection reliability and computational efficiency, outperforming traditional HCC and providing a lightweight alternative to GPU-dependent detectors such as Tiny-YOLO and MobileNet-SSD.
Co-Authors adani, M. Syakhisk N. Adipratama, Ivan Rafli Agung Setia Budi, Agung Setia Aji, Eko Prastowo Akbar, Rizky Alhamid, Faticha Kamila Audrian, Nathaniel Azizy, Faqih Barlian Henryranu Prasetio Bhanu Shidqianto, Naufal Butarbutar, Erlinda Chatarina Umbul Wahyuni Daffa, Ali Zhafran Dahnial Syauqy Dananjaya, Andreas Wirawan Daniswara, Muhammad Ananta Dewa, Muklas Isa Eko Setiawan Fabiana, Ryzaldi Ananda Faiqoh, Rifdah Fauzi, Muhammad Aqil Fuad, Alifa Azwadina Gabriel Hizkia Marhata Simbolon Gembong Edhi Setyawan Ghifari, Achmad Giffary, Muhammad Zufar Hakim, Muhammad Ilham Hanafi, Muhammad Imam Hanifa Maulani Ramadhan Hurriyatul Fitriyah, Hurriyatul IKA NURFARIDA J'Verron, Shadam Ksatria, Fatih Raka Kurnia, Yudisthira Dwi Muhammad Fahrizal, Muhammad Musharrif, Mohammad Faiz Nashrullah, Ikbar Razan Nugraha, Aldia Agri Palwa Luktagara, Abim Renara Perkasa, Septiyo Budi Prakoso, Raihan Pramana, Danish Gyan Prasetyo, Ragil Hadi Ratanasari, Dittha Rava Athalla, Muhammad Rizal Maulana, Rizal Rohman, Zahra Auliaul Rohmandzoni, Achmad Santoso, Teguh Surya Darma Saputra, Naufal Rijaldi Shabiyya, Syifa’ Hukma Simbolon, Gabriel Hizkia Marhata Surya Darma Santoso, Teguh Syahrul Chilmi, Syahrul syauqi Toar, Mikhael Ryan Trido, Pradytia Vebrianto, Yudaneru Yasa Palaguna Umar, Yasa Palaguna Yunus, Ahmad Haykal