PENGEMBANGAN SISTEM PERHITUNGAN JUMLAH KENDARAAN BERDASARKAN JENIS KENDARAAN MENGGUNAKAN ALGORITMA YOLO SECARA REALTIME - Kusuma, Adri Surya; Pradana, Afu Ichsan; Pamekas, Bondan Wahyu (Universitas Budi Luhur, 2024)
This study developed a vehicle counting system based on vehicle types using the YOLO algorithm in real-time. With the advancement of artificial intelligence technology, particularly in computer vision, outdoor media has also advanced through the computation of CCTV image results installed on such media. The significance of this research is found in its ability to provide accurate and real-time traffic data that can be utilized by local governments, traffic management companies, property developers, and advertising companies for traffic planning, security, and marketing analysis. The research method involved training the YOLOv9 model and centroid-based tracking on a dataset that included four vehicle classes: car, motorcycle, bus, and truck. The results showed that the developed system could detect and track vehicles with high accuracy, achieving a mAP of 0.9, precision of 0.909, recall of 0.828, and an F1-Score of 0.867. However, the system's performance heavily depends on the hardware specifications used, and the detection for motorcycles has lower evaluation scores compared to other vehicle types. This study indicates that improving hardware specifications and optimizing the model can enhance system performance. These findings are important as they reveal the considerable potential of using the YOLO algorithm in real-time traffic monitoring applications.