MSCF-Net: Attention-Guided Multi-Scale Context Feature Network for Ship Segmentation in Surveillance Videos

Abstract

With the advent of artificial intelligence, ship segmentation has become a critical component in the development of intelligent maritime surveillance systems. However, due to the increasing number of ships and the increasingly complex maritime traffic environment, the target features in these ship images are often not clear enough, and the key details cannot be clearly identified, which brings difficulty to the segmentation task. To tackle these issues, we present an approach that leverages state-of-the-art technology to improve the precision of ship segmentation in complex environments. Firstly, we employ a multi-scale context features module using different convolutional kernels to extract a richer set of semantic features from the images. Secondly, an enhanced spatial pyramid pooling (SPP) module is integrated into the encoder’s final layer, which significantly expands the receptive field and captures a wider range of contextual information. Furthermore, we introduce an attention module with a multi-scale structure to effectively obtain the interactions between the encoding–decoding processes and enhance the network’s ability to exchange information between layers. Finally, we performed comprehensive experiments on the public SeaShipsSeg and MariBoatsSubclass open-source datasets to validate the efficacy of our approach. Through ablation studies, we demonstrated the effectiveness of each individual component and confirmed its contribution to the overall system performance. In addition, comparative experiments with current state-of-the-art algorithms showed that our MSCF-Net excelled in both accuracy and robustness. This research provides an innovative insight that establishes a strong foundation for further advancements in the accuracy and performance of ship segmentation techniques.

Keywords

• ship segmentation
• multi-scale context feature
• spatial pyramid pooling
• attention module
• deep learning