OPTIMASI KLASIFIKASI DETEKSI SENJATA API PADA ANIMASI BLACK LAGOON DENGAN ALGORITMA DEEPSORT DAN KALMAN FILTER - Dadang Iskandar Mulyana; Agus Sigit Sumarsono (Jurnal Indonesia : Manajemen Informatika dan Komunikasi, 2023)
As technology develops, especially in the field of artificial intelligence, many new systems and programs are created with artificial intelligence, one of which is the field of computer vision, namely the detection of objects ranging from transportation, humans, masks, animals, and even firearms detection systems. However, this system still has deficiencies in the level of accuracy when complex conditions such as lighting and others. Currently firearms detection systems use datasets in the form of photos of real weapons, rarely or even no one uses other datasets such as games or animations to determine the performance of the detection system. This study aims to determine the performance of the firearms detection and classification system by experimenting with objects from an animation with a public dataset from the website www.imfdb.org/Black_Lagoon as well as several scenes from other animations and also an animation entitled Black Lagoon, then optimization is carried out.with the DeepSORT algorithm and Kalman Filter assisted by the YOLOv3 detection system, several percentage values were obtained such as a mAP value of 82.13%, 81.19% precision, and 78.53% recall in the training dataset using 100 iterations of epochs, and in the training model got a loss value of 8.27% and 6.67% for the val_loss value using 50 iterations of epochs.