Ship Detection in Satellite Images using You Only Look Once-Neural Architecture Search

Ship Detection in remotely sensed satellite imagery is a challenging task due to tiny size of the objects and low resolution of the images. Several object detection methods that perform very well for medium-sized and large-sized objects, miserable fail to perform decently in such applications. From the vast variety of the models available, few You only look once (YOLO) have proven to yield decent efficiency and accuracy in ship detection in satellite images. Hence, the paper proposes to detect and classify ships in remotely sensed satellite images by applying transfer learning to fine-tune the latest YOLO-Neural Architecture Search (YOLO-NAS) model. To the best of our knowledge this paper is the first attempt to test YOLO-NAS model for ship detection in satellite imagery. To demonstrate the strength of the YOLO NAS model, the paper also trains YOLOv5 and YOLOv8 on the same dataset allowing to comprehensively compare the efficiency of the proposed model against previous state-of-art versions. The findings demonstrate high mean average precision scores for the proposed model, indicating the effectiveness of the YOLO-NAS algorithm in accurately locating ships in satellite images.