A Performance Comparison of State-of-the-Art Models in Ship Detection from Aerial Images - Bui, Quang Dung; Dang, Kinh Bac; Nguyen, Manh Ha; Hoang, Thi Huyen Ngoc; Tuan Linh, Giang; Giang, Van Trong; Yasir, Mohamad; Tong, Phuc Tuan; Nguyen, Cong Long; Nguyen, Thu Nhung; Nguyen, Thai Son; Tran, Khanh Toan; et al. (Vietnam Journal of Marine Science and Technology, 2026)
In the face of rapidly advancing technology, environmental concerns, and societal changes, the maritime sector faces many challenges in providing safe, environmentally friendly shipping, fishing, and offshore services. The dynamic nature of fishing zones, the limited technology aboard some vessels, the lack of data in developing regions, and the prevalence of illicit activities all contribute to the difficulty of ship detection in maritime environments. This research presents an optimized deep learning model to tackle these issues head-on, thereby improving the ship detection system’s precision and effectiveness in Vietnam using high-resolution images acquired by the Worldview-2 sensor. The research seeks to answer questions about real-time vessel and fishing-area detection and to investigate the future distribution of fishing areas. Three network architectures, including Fast R-CNN, YOLOv9, and SSD, generate twelve models. After optimization, the model with a Fast R-CNN architecture, 128 × 128 input size, and no non-maximum suppression (NMS) was used to detect ships and estimate their density in central Vietnam, achieving 96% accuracy. YOLOv9 (128 × 128) should be used for real-time vessel detection despite the model’s accuracy of approximately 92%, which can be crucial for managing fisheries activities in other regions.