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All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar CYCLOTRON Emerging Information Science and Technology Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) JOINCS (Journal of Informatics, Network, and Computer Science) Computing Insight: Journal of Computer Science Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Khidmatuna: Jurnal Pengabdian Masyarakat
Muhamad Amirul Haq
Universitas Muhammadiyah Surabaya
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Mobile Surveillance System using Unmanned Aerial Vehicle for Aerial Imagery Haq, Muhamad Amirul
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24837

Abstract

Crowd counting plays a vital role in public safety, particularly during riot scenarios where understanding crowd dynamics is crucial for effective decision-making and risk mitigation. Accurate crowd estimation in such environments enables authorities to monitor the situation in real time, allocate resources efficiently, and prevent potential escalations. However, counting individuals in a riot scenario presents unique challenges due to the chaotic nature of the scene, varying crowd densities, and obstructions caused by movement and environmental factors. Traditional methods struggle to provide reliable results in these conditions, necessitating advanced solutions. This study explores the implementation of CSRNet (Congested Scene Recognition Network), a state-of-the-art deep learning model, to address crowd counting in challenging environments characterized as "images in the wild." CSRNet’s ability to leverage dilated convolutions allows it to effectively capture contextual information and handle high crowd densities without sacrificing spatial resolution. We evaluate the model’s performance on diverse datasets, including aerial imagery and real-world riot scenarios, focusing on its adaptability to dynamic, unstructured environments. The results demonstrate the potential of CSRNet to provide accurate crowd density estimates under adverse conditions, offering critical insights for public safety applications. By addressing the technical challenges of implementing CSRNet in these contexts, this study contributes to the advancement of deep learning-based crowd counting, emphasizing its significance in real-world scenarios such as riots and other high-stakes events. Future work aims to further enhance the model's robustness and applicability to diverse operational settings.
Region Enhanced Edge-Based Multi-Class Object Proposal for Self-Driving Vehicles Muhamad Amirul Haq; Le Nam Quoc Huy; Muhammad Ridlwan
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4662

Abstract

On-road object detection is a fundamental element for the safety and reliability of autonomous driving systems. A primary challenge is developing object detection algorithms that are both fast and robust. This paper introduces a novel object proposal algorithm, named Region Enhanced Edge-Based (REEB) proposal, designed to accelerate object detection by significantly reducing the number of candidate regions requiring evaluation by a subsequent classification network. REEB leverages edge-map cues to score and rank initial proposals. To further enhance both detection quality and processing speed, the algorithm integrates efficient complementary techniques: image entropy is used to guide proposal generation density in relevant image regions, and road segmentation aids in refining proposal scores by differentiating road from non-road areas. Experimental evaluations on the KITTI dataset demonstrate that REEB achieves an average recall rate of 72.1% across four classes (pedestrian, cyclist, car, and truck) with an average processing time of 15 milliseconds per image. These results indicate strong performance when compared to other traditional, non-deep learning object proposal algorithms.
Co-Authors Akbar, Ilhaam Syafruddin Aswin Rosadi Binuri, Moch. Ghulam Abrari Dina, Dinda Franciska Mey Erie Kresna Andana Farid Wahyu Wicaksono Fayyadh, Alwan Firman Abdullah, Hafiz Hariyanti, Tining Haryanti, Tining Hendra Kusuma Huy, Le Nam Quoc Ilhaam Syafruddin Akbar Ivana, Elmira Jeff L. Gaol Kusumajanti, Wahju Le Nam Quoc Huy Lutfiyan Nurdianah M. Ridlwan Muhammad Ridlwan Muhammad Rivai Naila, Ishmatun Nuniek Fahriani, Nuniek Nurdin, Farid Rahman Oktaviani, Marista Ridlwan, Muhammad Riyadi, Muhammad Akmal Fijar Sigit Armiyanto Sulton Dedi Wijaya Wahju Kusumajanti Wicaksono, Farid Wahyu