SMPISD-MTPNet: Scene Semantic Prior-Assisted Infrared Ship Detection Using Multi-Task Perception Networks
Chen Hu, Xiaogang Dong, Yian Huang Lele Wang, Liang Xu, Tian Pu,, Zhenming Peng

TL;DR
This paper introduces SMPISD-MTPNet, a novel multi-task perception network that leverages scene semantic priors and a new dataset to improve infrared ship detection accuracy in complex scenes.
Contribution
The paper proposes a new multi-task perception network with scene semantic extraction and a novel dataset for infrared ship detection, outperforming existing methods.
Findings
Outperforms state-of-the-art methods in infrared ship detection.
Effectively detects small and dim targets in complex scenes.
Introduces the IRSDSS dataset with scene segmentation annotations.
Abstract
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…
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Taxonomy
TopicsInfrared Target Detection Methodologies · Remote-Sensing Image Classification · Advanced Neural Network Applications
MethodsSoftmax · Attention Is All You Need
