Marine ship recognition is a challenging Fine-Grained Visual Categorization (FGVC) problem due to the large visual variations caused by motion blur, occlusion, lighting changes, and etc. The visual distinction between similar categories is usually very small, so it is difficult to solve it with general recognition algorithm. It demands an advanced discriminative model to accurately segment marine ships from the backgrounds and classify the type of the ship. However, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose to recognize marine ship based on two cascade CNNs (convolutional neural networks), a shallow CNN and a deep CNN. The shallow CNN is used to quickly remove most of the background regions to reduce the computation cost, and the deep CNN is used to classify the type of ship in the remaining regions. The two CNNs are trained end-to-end, and they are complementary to each other to guarantee the recognition precision with low computation cost. Experimental results show that the proposed method is promising for marine ship recognition.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.