Deep convolutional neural networks for ship detection using refined DOTA and TGRS-HRRSD high-resolution image datasets

Abstract

Automated identification of ships in satellite imagery is of utmost significance in many military and civilian applications, including monitoring maritime traffic and maintaining maritime security. However, ship detection from remotely sensed imagery is a challenging task because of their varying sizes, orientations, types, and various interferences. In recent years, deep learning algorithms have become a powerful tool in image processing applications, including image classification and object detection due to their strong feature representation capabilities. In this study, a refined ship dataset, containing a total of 13,735 instances and representing different ship types, was generated using state-of-the-art datasets for the ship detection task. In a comparative framework, the performance of five deep learning algorithms (Faster R-CNN, YOLOv8, CenterNet, EfficientDet, and SSD) in ship detection was investigated using the created dataset. The performance of the deep learning models was compared using MS COCO accuracy metrics. In addition, the generalization capabilities of the models were tested on an independent image acquired by Gokturk-1 having a mucilage effect that undermines the spectral discrimination. Results revealed that the YOLOv8 model had the greatest ship detection performance (AP@0.50 = 96.33 %), followed by CenterNet (AP@0.50 = 88.80 %), Faster R-CNN (AP@0.50 = 85.30 %), EfficientDet (AP@0.50 = 78.91 %) and SSD (AP@0.5 = 75. 98 %), and obtained the highest accuracies in terms of other COCO metrics. In addition, generalization capabilities on the test image confirmed the superiority of the YOLOv8 model with AP@0.50 score of 63.93 %. Overall, the results of this study validated the effectiveness of the YOLOv8 model in identifying ship targets in remotely sensed images. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.