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All Journal Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya
Arief Setyanto
Magister Teknik Informatika, Universitas Amikom Yogyakarta
Agriculture, Biological Sciences & Forestry Education Mathematics Physics Public Health Other

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Deteksi Kapal Menggunakan Algoritma DC-YOLOV8 Freddy Winarso; Arief Setyanto; Anggit Dwi Hartanto
Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya Vol. 5 No. 4 (2026): Lambda
Publisher : Lembaga Bale Literasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58218/lambda.v5i4.1926

Abstract

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.
Co-Authors Anggit Dwi Hartanto Freddy Winarso