Adaptive Multiscale Enhanced Interaction Network for Infrared Iceberg Detection

Icebergs threaten the navigation safety of ships in the Arctic, small icebergs are difficult to detect in harsh sea conditions. Timely detection of these targets is crucial for aiding ships in navigational decisions. Infrared detection technology is an effective solution for detecting icebergs. Due to the low contrast of iceberg infrared images and severe sea wave clutter, current methods can not suit iceberg detection tasks well. In this article, we propose an Adaptive Multi-scale Enhanced Interaction Network (AMEINet) and construct the Single-Frame Infrared Iceberg Target Dataset (SFIIT). First, the Global Contrast Enhance Module (GCE) is designed, which adaptively enhances the image with the target as the center, and reconstructs the potential features submerged by low contrast. Then, to obtain rich features, the Boundary Feature Extraction Module (BFE) and Gated Context Feature Extraction Module (GCFE) are designed to extract target information at different levels. BEF adopts a new computation method to obtain the edge contour structures without edge GroundTruth supervision. GCFE effectively controls redundant information transmission through the gate mechanism and jump connection, filtering out the false alarms and miss detection caused by strong wave clutter. Finally, experiments on the publicized SIRST dataset and proposed SFIIT dataset show that the proposed method can achieve superior performances in infrared iceberg detection.