Efficient Ship Detection System for Maritime Surveillance using Deep Learning Approach

The usage of drones in maritime surveillance is a very effective means to observe ships. As the ship detection is vital for applications such as port monitoring, cross-border surveillance, Moreover, it is not only vital for maritime surveillance but also environmental conservation. Despite its effectiveness, the drone-captured images present itself with its own challenges, specifically when leveraging on a custom dataset which is tailored for specific application. To address the issues, we propose a novel ship detection approach called YOLOv9 with Adan optimizer (YOLOv9-Adan) which is more accurate to increase the efficiency of maritime surveillance. The YOLOv9-Adan model integrates the robust object detection capabilities of the adaptive learning capabilities of the Adan (ADAptive Nesterov momentum algorithm) optimiser, trained on the drone image dataset comprising 3200 images of maritime scenes and ship types in drone view. Our model is trained on a drone-image dataset comprising 3200 images of maritime scenes and ship types in drone views collected from various sources. The experimental results show that our approach using the YOLOv9-Adan model achieves 65.5% mAP, which exceeds the mAP of YOLOv9 by 4.3%. Additionally, This article also provides a comparative analysis of our model YOLOv9-Adan with other existing models in literature with consistently surpassing existing approaches