Maritime Infrared Ship Detection in AAV Imagery Based on Two-Stage Region-Segmentation-Guided Learning Network

Infrared (IR) ship detection in unmanned aerial vehicles (UAV) imagery under maritime environments encounters challenges due to the complexity of sea-sky backgrounds. Current research efforts have not fully leveraged the unique characteristics of maritime scenes to distinguish this task from general IR target detection. To overcome these limitations, we propose a Two-Stage Sea-Sky Region-Segmentation-Guided IR Ship Enhancement and Detection (TIRSD) learning network, specifically designed for targeted ship enhancement and detection in maritime environments. The process is structured into two main stages: enhancement and detection. In the enhancement stage, pixel-level sea-sky region segmentation is accomplished via sea-sky line (SSL) detection, which is followed by precise IR ship enhancement and interference suppression, utilizing specially designed morphological filters. For the detection stage, the Bilateral Hierarchical Target Encoding Module (BHTEM) is introduced to encode both high- and low-dimensional features, ensuring optimal detail retention and extraction. Additionally, the Iterative Fusion Enhancement Midlayer (IFEM) with neighborhood reinforcement operations is presented. Furthermore, a newly controllable Local Discrete Attention Mechanism (LDAM) is utilized to focus on small ships, thereby improving detection efficiency. TIRSD surpasses the current leading methods in terms of accuracy, achieving a Mean Average Precision (mAP) of 76.9% on the proposed dataset and 52.4% on a public dataset. Comprehensive experimental results underscore TIRSD’s effective utilization of the unique characteristics of maritime environments, indicating promising application prospects.