Helmet Detection System for Work Safety  Using YOLOv8 Algorithm

Iyung Fachrur Rozi; Anggay Luri Pramana

doi:10.24036/voteteknika.v13i2.133653

Helmet Detection System for Work Safety Using YOLOv8 Algorithm

Iyung Fachrur Rozi⁽¹⁾, Anggay Luri Pramana⁽²⁾,
(1) Universitas Nahdlatul Ulama Sidoarjo

Indonesia
(2) Universitas Nahdlatul Ulama Sidoarjo

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/voteteknika.v13i2.133653

Full Text:

Language : id

Abstract

This study develops a helmet detection system using the YOLOv8 algorithm to support automatic work safety monitoring. A total of 4,924 training images were used, yielding 1,407 valid images after filtering. The system detects helmet objects with confidence levels ranging from 0.73 to 0.85 and processes images within an average of 0.65 seconds. The model achieved an F1-score of 91.1% and a precision of 0.95. This real-time detection system is expected to enhance safety compliance in workplaces and traffic environments.

References

Hadi Supriyanto, Sarosa Castrena Abadi, & Aliffa Shalsabilah. (2024). Deteksi Helm Keselamatan Menggunakan Jetson Nano dan YOLOv7. Journal of Applied Computer Science and Technology, 5(1), 1–8. https://doi.org/10.52158/jacost.v5i1.637

Yanto, Y., Aziz, F., & Irmawati, I. (2023). Yolo-V8 Peningkatan Algoritma Untuk Deteksi Pemakaian Masker Wajah. JATI (Jurnal Mahasiswa Teknik Informatika), 7(3), 1437–1444. https://doi.org/10.36040/jati.v7i3.7047

Barlybayev, A., Amangeldy, N., Kurmetbek, B., Krak, I., Razakhova, B., Tursynova, N., & Turebayeva, R. (2024). Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study. Cogent Engineering, 11(1). https://doi.org/10.1080/23311916.2024.2333209

Rahmawati et al., “Penerapan YOLOv7 untuk Deteksi Kebakaran Real-time di Area Industri,” Jurnal Teknologi Industri, vol. 10, 2024.

Fasounaki, M., Yüce, E. B., Öncül, S., & Ince, G. (2021). CNN-based Text-independent Automatic Speaker Identification Using Short Utterances. Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021, 01, 413–418. https://doi.org/10.1109/UBMK52708.2021.9559031

Ardiansyah et al., “Evaluasi Akurasi dan Presisi Model YOLOv8 dalam Deteksi Kesegaran Buah,” JUPITER, vol. 16, no. 2, 2024

Ceridwen Johnson. (2020). WHO/ILO: Almost 2 million people die from work-related causes each year. Who. https://www.who.int/news/item/17-09-2021-who-ilo-almost-2-million-people-die-from-work-related-causes-each-year

Jannah, aufa rosiatul. (2024). Sistem penghitung penumpang kendaraan menggunakan kamera berbasis SSD. Αγαη, 15(1), 37–48.

Muhammadiyah, U., Aceh, M., & Nusantara, U. B. (2024). Penggunaan Algoritma Support Vector Machine ( SVM ) Untuk Deteksi Penipuan pada Transaksi Online. 13, 1627–1632.

Nurhakiki, J., Yahfizham, Y., William, J., Ps, I. V, Estate, M., Percut, K., Tuan, S., & Serdang, K. D. (2024). Studi Kepustakaan: Pengenalan 4 Algoritma Pada Pembelajaran Deep Learning Beserta Implikasinya. Jurnal Pendidikan Berkarakter, 1, 270–281. https://doi.org/10.51903/pendekar.v2i1.598

Pratomo, M. H., Ilmi, N., & Wibowo, S. A. (2024). Risiko Keselamatan dan Kesehatan Kerja ( K3 ) pada Perusahaan Konstruksi Menggunakan Metode Job Safety Analysis. 3(1), 14–18.

Satya, L., Septian, M. R. D., Sarjono, M. W., Cahyanti, M., & Swedia, E. R. (2023). Sistem Pendeteksi Plat Nomor Polisi Kendaraan Dengan Arsitektur Yolov8. Sebatik, 27(2), 753–761. https://doi.org/10.46984/sebatik.v27i2.2374

Terapan, H. P., Madenda, S., Lestari, D. P., Madenda, S., Octavia, L., Susetianingtias, D. T., Wibowo, H. T., Ligar, W., Kusuma, T. M., Suro, S., Rumambi, T., Prasetyo, E., Siswono, H., Yakti, B. K., Prayitno, R. H., Sudiro, S. A., Raharja, W. K., Hanif, A., & Baaits, A. (n.d.). Komputer Vision , Kecerdasan Artifisial , dan Sistem Tertanam.

Terven, J., Córdova-Esparza, D. M., & Romero-González, J. A. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680–1716. https://doi.org/10.3390/make5040083

Verdy, & Ery Hartati. (2024). Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Model Resnet-50. Jurnal Rekayasa Sistem Informasi Dan Teknologi, 1(3), 199–206. https://doi.org/10.59407/jrsit.v1i3.529