Main Article Content
Abstract
Fire spot detection in the graving dock area is crucial to prevent potentially harmful fires. This study employs the YOLOs method as a deep learning-based object detection technique to detect fire and sparks in real-time. Despite its high accuracy, visual interpretation of detection results remains challenging. Therefore, the Grad-CAM technique is utilized to generate a heatmap on the detection area of YOLO. The heatmap is calculated using the alpha blending method with a specific transparency factor, resulting in clearer visualization of detected objects. The test results show that the combination of YOLO and Grad-CAM can detect fire with an accuracy of 73%. The heatmap visualization validates the critical areas that contribute to the model's decision, making it suitable for fire monitoring systems in high-risk areas.
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Article Details
Copyright (c) 2025 Muhammad Thoriq Fadlol, Agus Khumaidi, Lilik Subiyanto, Joko Endrasmono, Mustika Kurnia Mayangsari, Anggarjuna Puncak Pujiputra
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
A. M. Adi, A. Khumaidi, M. B. Rahmat, D. P. Riananda, M. K. Hasin, and D. Sukoco, “Implementasi sistem deteksi titik api pada area graving dock menggunakan YOLOv5,” Jurnal Elektronika dan Otomasi Industri, vol. 11, no. 2, pp. 473–482, 2024. [Online]. Available: https://doi.org/10.33795/elkolind.v11i2.5233
T. Akhir, “Sistem peringatan dini tabrakan kapal secara realtime berbasis data Automatic Identification System (AIS),” 2022.
Y. Anggraini, D. Pasha, and A. Setiawan, “Sistem informasi penjualan sepeda berbasis web menggunakan framework CodeIgniter (studi kasus: Orbit Station),” Jurnal Teknologi dan Sistem Informasi (JTSI), vol. 1, no. 2, pp. 64–70, 2020. [Online]. Available: http://jim.teknokrat.ac.id/index.php/jtsi
Z. Arifin, M. Nurtanto, A. Priatna, N. Kholifah, and M. Fawaid, “Technology Andragogy Work Content Knowledge Model as a new framework in vocational education: Revised Technology Pedagogy Content Knowledge Model,” TEM Journal, vol. 9, no. 2, pp. 786–791, 2020. [Online]. Available: https://doi.org/10.18421/tem92-48
Z. Azmi and A. Pranata, “Implementasi IoT (Internet of Things) untuk spy jacket berbasis ESP32-CAM.” [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jskom
K. Burghardt and M. Forslund, “Fire detection system using infrared sensor and camera,” KTH Royal Institute of Technology, 2022.
M. D. Riski, “Rancang alat lampu otomatis di cargo compartment pesawat berbasis Arduino menggunakan push button switch sebagai pembelajaran di Politeknik Penerbangan Surabaya.”
H. F. Debri, “Rancang bangun sistem smart CCTV untuk efektivitas energi berbasis YOLO CNN dan Android di laboratorium otomasi PPNS,” Repository PPNS, 2019.
S. D. Kirtiana, M. Y. Santoso, and D. A. Subekti, “Analisis risiko hot work berbasis CSRA pada cargo oil tank kapal tanker,” in 7th Conference on Safety Engineering and Its Application, 2023.
E. D. Salsabillah, P. I. Siregar, and Habli, “Analisis terjadinya ledakan tangki slop port MT. Sri Asih saat pelaksanaan hotwork dalam proses drydock,” Meteor STIP Marunda. [Online]. Available: http://ejournal.www.stipjakarta.dephub.go.id
W. Fang, L. Wang, and P. Ren, “Tinier-YOLO: A real-time object detection method for constrained environments,” IEEE Access, vol. 8, pp. 1935–1944, 2020. [Online]. Available: https://doi.org/10.1109/access.2019.2961959
A. Febriandirza, “Perancangan aplikasi absensi online dengan menggunakan bahasa pemrograman Kotlin,” Jurnal Pseudocode, vol. 7, no. 2, 2020. [Online]. Available: www.ejournal.unib.ac.id/index.php/pseudocode
I. W. Gede, S. Suarjaya, and I. P. A. Saputra, “Deteksi api kebakaran berbasis computer vision dengan algoritma YOLO,” Journal of Applied Mechanical Engineering and Green Technology, vol. 3, pp. 53–58, 2022. [Online]. Available: https://ojs2.pnb.ac.id/index.php/jametech
R. G. Guntara, “Pemanfaatan Google Colab untuk aplikasi pendeteksian masker wajah menggunakan algoritma deep learning YOLOv7,” Jurnal Teknologi dan Sistem Informasi Bisnis, vol. 5, no. 1, pp. 55–60, 2023. [Online]. Available: https://doi.org/10.47233/jteksis.v5i1.750
A. Khumaidi et al., “Design of a fire spot identification system in the PT PAL Indonesia work area using YOLOv5s.”
A. Kirchknopf et al., “Explaining YOLO: Leveraging Grad-CAM to explain object detections.” [Online]. Available: https://doi.org/10.3217/978-3-85125-869-1-13
C. Lv et al., “A lightweight fire detection algorithm for small targets based on YOLOv5s,” Scientific Reports, vol. 14, Art. no. 14104, 2024.
Y. Wang et al., “Real-time detection of flame and smoke using an improved YOLOv4 network,” Signal, Image and Video Processing, vol. 16, pp. 1109–1116, 2022.
F. Romadloni, J. Endrasmono, Z. M. A. Putra, A. Khumaidi, I. Rachman, and R. Y. Adhitya, “Identifikasi Warna Buoy Menggunakan Metode You Only Look Once Pada Unmanned Surface Vehicle,” Jurnal Teknik Elektro dan Komputer TRIAC, vol. 10, no. 1, 2023. [Online]. Available: https://doi.org/10.21107/triac.v10i1.19650
C.-L. Ji, T. Yu, P. Gao, F. Wang, and R.-Y. Yuan, “YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5,” Feb. 2024. [Online]. Available: https://doi.org/10.1007/s11554-024-01519-4