From Trail to Target: Efficient Infrared Moving Ship Detection via Dual-Head Supervision to Break the Slicing Barrier

Moving ship detection is vital for real-time maritime monitoring. Nevertheless, several challenges arise in this area: (i) Wide-area images often need to be sliced into patches to detect tiny targets, which is inefficient. (ii) The ships are small with almost no texture, leading to difficulties in accurate detection. (iii) The contrast between ships and ocean is relatively low, resulting in weak features. Although moving ships exhibit weak features, they often possess distinct wake trails. Capitalizing on this characteristic, we tailored a dual-head supervision network for moving ship detection. Initially, a dual-head supervision architecture is introduced to guide the model in using wake trails for target localization, thereby addressing the inefficiency caused by slicing. Subsequently, the background association head and target confirmation head are introduced to collaboratively enhance detection accuracy by leveraging inter-head attention mechanism. Finally, to address the issues of weak features, the dynamic feature enhancement module is embedded into backbone to boost the model’s feature extraction capability for moving targets. Experiments on GaoFen-1 dataset demonstrated that our method significantly improved the efficiency and performance of infrared moving ship detection and reached the state-of-the-art performance. Source codes will be available at https://github.com/KTqizhi/KTqizhi.github.io.