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INTEGRASI YOLOV8S DAN RADAR FMCW BERBASIS ANTARMUKA PENGGUNA GRAFIS (GUI) UNTUK DETEKSI REAL-TIME PEJALAN KAKI DI ZEBRA CROSS
Keselamatan pejalan kaki merupakan isu penting karena tingginya angka kecelakaan lalu lintas, terutama di zebra cross. Meskipun teknologi seperti YOLOv8 menawarkan akurasi tinggi dalam deteksi objek, performanya menurun di kondisi minim cahaya, sedangkan Radar FMCW unggul di kondisi tersebut namun kesulitan mengklasifikasi objek yang tumpang tindih. Penelitian ini bertujuan mengintegrasikan YOLOv8 dan Radar FMCW untuk meningkatkan akurasi deteksi pejalan kaki secara real-time serta mengetahui performanya di berbagai lingkungan. Tujuan penelitian adalah mengembangkan sistem integrasi berbasis computer vision dan Radar FMCW untuk deteksi pejalan kaki, serta mengevaluasi performa sistem di ruang tertutup, terbuka, dan zebra crossing. Metode penelitian meliputi pengembangan modul YOLOv8 dan Radar FMCW, integrasi data melalui GUI, dan pengujian sistem dengan algoritma matching untuk klasifikasi akurasi. Proses ini diawali dengan studi literatur, perancangan sistem, dan kalibrasi parameter untuk memastikan hasil yang akurat. Hasil penelitian menunjukkan sistem bekerja optimal di ruang terbuka dengan nilai match tertinggi 25.55% pada jarak 300 cm dan selisih deteksi rata-rata 0.39. Namun, di zebra crossing, performa menurun dengan nilai match hanya 3.69% dan selisih deteksi 1.93, meskipun 27.54% deteksi mencapai partial match, menunjukkan potensi sistem dalam kondisi kompleks. Sistem ini juga mampu berjalan pada perangkat tanpa GPU khusus dengan frame rate 1.8-7 FPS, cocok untuk aplikasi edge computing. Pedestrian safety is an important issue due to the high number of traffic accidents, especially at zebra crossings. Although technologies such as YOLOv8 offer high accuracy in object detection, their performance degrades in low light conditions, while FMCW Radar excels in such conditions but struggles to classify overlapping objects. This research aims to integrate YOLOv8 and FMCW Radar to improve real-time pedestrian detection accuracy and analyze their performance in various environments. The research objectives are to develop an integrated system based on computer vision and FMCW Radar for pedestrian detection, and evaluate the performance of the system in closed, open, and zebra crossing spaces. The research method includes the development of YOLOv8 and FMCW Radar modules, data integration through GUI, and system testing with matching algorithm for accuracy classification. This process begins with a literature study, system design, and parameter calibration to ensure accurate results.The results show that the system works optimally in open spaces with the highest match value of 25.55% at a distance of 300 cm and an average detection difference of 0.39. However, at a zebra crossing, performance degraded with a match value of only 3.69% and a detection difference of 1.93, although 27.54% of detections achieved a partial match, demonstrating the potential of the system in complex conditions. The system is also capable of running on devices without a dedicated GPU with frame rates of 1.8-7 FPS, suitable for edge computing applications.
KODEPRODI20201#Teknik Elektro_S1, Universitas Pendidikan Indonesia
INTEGRASI YOLOV8S DAN RADAR FMCW BERBASIS ANTARMUKA PENGGUNA GRAFIS (GUI) UNTUK DETEKSI REAL-TIME PEJALAN KAKI DI ZEBRA CROSS (Text)
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TK Electrical engineering. Electronics Nuclear engineering