DFRI:DETECTION AND FINE-GRAINED RECOGNITION INTEGRATED NETWORK FOR INSHORE SHIP

Remote sensing ship detection is of great importance for the applications of maritime transport and precision guidance. However, the background interference on land, the large similarity between ship classes, and the arbitrary orientation of ships all pose serious challenges for inshore ship detection. To solve the problem of complex and diverse land backgrounds and sea conditions, we use the contextual attention module to enhance the ship target features. In addition, a rotation-invariant feature extraction and fusion module is introduced to make the features directionally invariant and increase the inter-class distance by fusing global features with local features. Finally, the feature decoupled module is redesigned to resolve the conflict between target detection and fine-grained recognition. Experimental results show that our proposed integrated model for inshore ship detection and fine-grained recognition improves mAP by about 6% compared to the current baseline model.