VEHICLE COUNTING BASED ON OBJECT DETECTION USING THE YOLOV4-TINY ALGORITHM: PENGHITUNG JUMLAH KENDARAAN BERBASIS DETEKSI OBJEK MENGGUNAKAN ALGORITMA YOLOV4-TINY - Rian Setiyana; Hadi Nasbey; Haris Suhendar (Joint Prosiding IPS dan Seminar Nasional Fisika, 2025)
Traffic congestion occurs when vehicle flow is disrupted or completely halted. This situation is often encountered in urban areas, especially when there is an imbalance between the number of vehicles and the available road capacity. Compared to the rate of urban road infrastructure development, the increase in the number of vehicles has grown exponentially. From 2013 to 2017, the average annual increasein the number of vehicles was approximately 8.600.000. This has led to high levels of traffic congestion in urban areas. Therefore, this study aims to monitor traffic density by developing a vehicle counting system based on object detection. This system is designed to count the number of vehicles in video recordings. In this study, the vehicle counting system utilizes the object detection capabilities of the You Only Look Once (YOLO) algorithm, specifically YOLOv4-tiny. YOLOv4-tiny was chosen for its smaller model size and faster detection capabilities. Based on the training results, the mAP of the best YOLOv4-tiny weights reached 95.13%. Furthermore, the counting results indicate that the system successfully counted vehicles passing the auxiliary line based on their types.