Deteksi Kapal Menggunakan Algoritma DC-YOLOV8 - Freddy Winarso; Arief Setyanto; Anggit Dwi Hartanto (Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya, 2026)
This study aims to improve the inference time and accuracy of ship detection in complex maritime imagery using the DC-YOLOv8 algorithm. DC-YOLOv8 is an enhanced version of YOLOv8 specifically designed for real-time detection under challenging visual conditions, such as low lighting, fog, and dynamic sea backgrounds. The method integrates an MDC module, depthwise separable convolution, and an updated feature fusion strategy. The evaluation was conducted using the SeaShips dataset and compared with the baseline YOLOv8 model. Experimental results show that DC-YOLOv8 achieves a significant improvement in inference time, reducing it by 2.855 ms, while maintaining a competitive mAP score of 0.7953. These results indicate that DC-YOLOv8 is more effective for real-time detection tasks in maritime environments.