Multiple models collaboration for ship detection

In the context of globalization, the importance of ship monitoring is becoming more and more prominent. With the continuous progress of the remote sensing imaging technology, ship detection has become a key means to ensure the safety and efficiency of maritime transportation, and is crucial for maritime transportation, environmental protection, and national security. However, due to the large difference in scales and complex background of ship targets, existing single detection model methods rely too heavily on training data and cannot adapt to ship targets with variable scales. In this paper, we propose a multiple models collaboration framework, in which multiple trained ship detection models are regarded as auxiliary network, and the main network training is optimized by knowledge migration. First, ternary relationship constraints are introduced to transfer knowledge between the auxiliary network and the main network. Then, a soft-label guidance strategy is proposed to further improve the accuracy of ship detection. The experimental results show that compared with the existing mainstream methods, the proposed method demonstrates better performance on DOTA and xView datasets, overcoming the limitation of a single model and providing a new solution idea for target detection in remote sensing images.