A Robot Model for Detecting Smoking Violations Using YOLOv5 and PID-Based Navigation Control

Selamat Muslimin; Muhammad Andaru Megaarta; Rayhan Triandika

doi:10.24014/ijaidm.v8i2.37345

A Robot Model for Detecting Smoking Violations Using YOLOv5 and PID-Based Navigation Control

Selamat Muslimin, Muhammad Andaru Megaarta, Rayhan Triandika

Abstract

Smoking violations in restricted areas, especially in public spaces exposed to secondhand smoke, remain a significant concern. This study develops an autonomous robot designed to detect smoking violations using YOLOv5 and Raspberry Pi. The robot's camera captures real-time images to identify smoking behavior, with YOLOv5 accurately detecting cigarette objects. For navigation, the robot employs a PID control system, complemented by an encoder and a compass sensor, ensuring precise movement. The results demonstrate that the robot achieves a confidence level of 87% in detecting smoking behavior at a distance of 250 cm, with a frame rate of 8 FPS. The PID-based navigation system ensures minimal error of ±5 cm over a 2-meter distance. These findings emphasize the robot's effectiveness in both detecting smoking violations and navigating accurately, making it an effective tool for the enforcement of smoke-free zone regulations.

Keywords

Autonomous Robot; Object Detection; PID; Smoking Violation; YOLOv5

References

H. Qudus and E. N. Hadi, “Overview of the Implementation of the No Smoking Area (KTR) Policy in the Indonesian Campus Environment: Literature Review,” J. Soc. Res., vol. 2, no. 6, pp. 1916–1928, 2023, doi: 10.55324/josr.v2i6.941.

W. Sulistiadi, M. Veruswati, A. Asyary, M. H. Herawati, R. A. Wulandari, and B. Haryanto, “Smoke-free zone in indonesia: Who is doing what now,” Open Access Maced. J. Med. Sci., vol. 8, no. E, pp. 322–324, 2020, doi: 10.3889/oamjms.2020.4091.

A. M. Fathoni, E. Zuliarso, and U. S. Semarang, “Implementation Of Yolov5 Method In The Cigarette Detection,” vol. 7, pp. 1449–1454, 2024.

L. Arief, A. Z. Tantowi, N. P. Novani, and T. A. Sundara, “Implementation of YOLO and smoke sensor for automating public service announcement of cigarette’s hazard in public facilities,” 2020 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2020 - Proc., pp. 101–107, 2020, doi: 10.1109/ICITSI50517.2020.9264972.

A. N. Albab and E. Rahmawati, “Rancang Bangun Sistem Navigasi Mobile Robot Berbasis Sensor Rotary Encoder Menggunakan Metode Odometri,” J. Inov. Fis. Indones., vol. 08, no. 2017, pp. 23–27, 2019.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.

M. Aditya, P. R. Gudpati, K. S. S. Reddy, P. shu, and R. Karampudi, “Smoking Detection using Deep Learning,” Int. J. Comput. Trends Technol., vol. 71, no. 02, pp. 8–14, 2023, doi: 10.14445/22312803/ijctt-v71i2p102.

A. Pundhir, D. Verma, P. Kumar, and B. Raman, “Region Extraction Based Approach for Cigarette Usage Classification Using Deep Learning,” Commun. Comput. Inf. Sci., vol. 1568 CCIS, pp. 378–390, 2022, doi: 10.1007/978-3-031-11349-9_33.

I. Sayekti, B. Supriyo, S. Warjono, S. B. Kuntardjo, and V. S. Kartika, “Rancang Bangun Sensor Kompas Sebagai Pendeteksi Sudut Orientasi Robot Beroda,” J. Elkolind, vol. 8, no. 3, pp. 397–405, 2022, doi: 10.33795/elkolind.v8i3/259.

S. Muslimin, E. Prihatini, N. L. Husni, and S. Pebrian, Development Of a Waypoint Navigation System For Rvm Robots Using Path Planning Algorithms Waypoint-Based Path Planning Method. Atlantis Press International BV, 2025. doi: 10.2991/978-94-6463-678-9.

M. R. Sagita, A. Ma, C. Rekik, W. Caesarendra, and R. Majdoubi, “Motion System of a Four-Wheeled Robot Using a PID Controller Based on MPU and Rotary Encoder Sensors,” vol. 2, no. 2, 2024, doi: 10.59247/csol.v2i2.150.

A. R. Putri, P. Nurrahayu, and A. Anas, “Robot Navigation Control System Using Hmc5883L,” JAREE-Journal Adv. Res. Electr. Eng., vol. 3, no. 1, pp. 61–66, 2019.

M. Wang, D. Qu, Z. Wu, A. Li, N. Wang, and X. Zhang, “Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5,” Sensors, vol. 24, no. 22, 2024, doi: 10.3390/s24227190.

S. System, “Smoking Dataset,” Apr. 2025, Roboflow. [Online]. Available: https://universe.roboflow.com/survalince-system/smoking-znv6l

E. Karantoumanis, V. Balafas, M. Louta, and N. Ploskas, “Computational comparison of image preprocessing techniques for plant diseases detection,” 7th South-East Eur. Des. Autom. Comput. Eng. Comput. Networks Soc. Media Conf. SEEDA-CECNSM 2022, pp. 8–12, 2022, doi: 10.1109/SEEDA-CECNSM57760.2022.9932972.

S. S. Killikatt, A. Patil, S. Pharakate, S. Koli, S. Wankhade, and M. Patil, “Object Detection and Image Annotation Using Deep Learning,” Int. Res. J. Mod. Eng. Technol. Sci., no. 06, pp. 1598–1607, 2023, doi: 10.56726/irjmets41723.

M. H. Ashar and D. Suarna, “KLIK: Kajian Ilmiah Informatika dan Komputer Implementasi Algoritma YOLOv5 dalam Mendeteksi Penggunaan Masker Pada Kantor Biro Umum Gubernur Sulawesi Barat,” Media Online, vol. 3, no. 3, pp. 298–302, 2022, [Online]. Available: https://djournals.com/klik

R. A. Setiawan and A. Setyanto, “Evaluasi Trade-off Akurasi dan Kecepatan YOLOv5 dalam Deteksi Kebakaran pada Edge Devices,” vol. 5, no. 11, pp. 4647–4655, 2024.

W. W. Sakti et al., “SKYHAWK: Jurnal Aviasi Indonesia Pengembangan Sistem Deteksi Otomatis FOD dengan YOLOv5 di Lingkungan Landasan Bandara,” vol. 3, no. 2, 2023, [Online]. Available: http://ejournal.icpa-banyuwangi.ac.id/index.php/skyhawk

M. Dafa Maulana, “Evaluasi Kinerja YOLOv8 dalam Identifikasi Kesegaran Ikan dengan Metode Deteksi Objek,” vol. 11, no. 4, pp. 2864–2869, 2024.

DOI: http://dx.doi.org/10.24014/ijaidm.v8i2.37345

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats