A Robot Model for Detecting Smoking Violations Using YOLOv5 and PID-Based Navigation Control - Muslimin, Selamat; Megaarta, Muhammad Andaru; Triandika, Rayhan (Indonesian Journal of Artificial Intelligence and Data Mining, 2025)
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.