  Infrared ship detection (IRSD) has received increasing attention in recent
years due to the robustness of infrared images to adverse weather. However, a
large number of false alarms may occur in complex scenes. To address these
challenges, we propose the Scene Semantic Prior-Assisted Multi-Task Perception
Network (SMPISD-MTPNet), which includes three stages: scene semantic
extraction, deep feature extraction, and prediction. In the scene semantic
extraction stage, we employ a Scene Semantic Extractor (SSE) to guide the
network by the features extracted based on expert knowledge. In the deep
feature extraction stage, a backbone network is employed to extract deep
features. These features are subsequently integrated by a fusion network,
enhancing the detection capabilities across targets of varying sizes. In the
prediction stage, we utilize the Multi-Task Perception Module, which includes
the Gradient-based Module and the Scene Segmentation Module, enabling precise
detection of small and dim targets within complex scenes. For the training
process, we introduce the Soft Fine-tuning training strategy to suppress the
distortion caused by data augmentation. Besides, due to the lack of a publicly
available dataset labelled for scenes, we introduce the Infrared Ship Dataset
with Scene Segmentation (IRSDSS). Finally, we evaluate the network and compare
it with state-of-the-art (SOTA) methods, indicating that SMPISD-MTPNet
outperforms existing approaches. The source code and dataset for this research
can be accessed at https://github.com/greekinRoma/KMNDNet.
