Infrared small target detection is a key task in computer vision and plays a crucial role in military defense, aerospace, and maritime surveillance. However, it remains a challenging task due to the small target size, low contrast, and complex background noise. Traditional methods, despite the progress made, still suffer from limited robustness and generalization capabilities. On the other hand, deep learning-based methods may fail to accurately recognize deep small targets due to insufficient background noise suppression. For this reason, we propose a parallel refinement network (PRNet). This network uses MobileNet v3 as the backbone for feature extraction, and then we propose the selective feature enhancement module (SFEM) to selectively enhance the extracted features to effectively improve the representativeness of the target features while suppressing the background interference. Finally, we propose the parallel refinement module (PRM) to optimize and aggregate features at different levels to achieve high accuracy and enhance the robustness of object detection. Experimental results on the public dataset MDFA show that the proposed method outperforms the current state-of-the-art methods in terms of detection performance.

Infrared Small Target Detection (IRSTD) plays a critical role in various fields such as military surveillance, autonomous driving, and environmental monitoring. However, traditional IRSTD methods often face high false alarm rates and missed detections when dealing with complex backgrounds, significantly limiting their effectiveness in real-world applications. Although recent advancements in deep learning-based IRSTD techniques have achieved remarkable progress, the detected target edges are often unclear and tend to be overly smooth. Additionally, when detecting extremely small targets, missed detections remain prevalent due to interference from background clutter. To address these issues, we propose a novel Edge-Aware Network (EANet). In the encoder stage, EANet employs a combination of residual blocks and max-pooling layers to extract feature maps containing multi-scale information. To further enhance the edge features of targets, we propose an edge-aware enhancement module (EAEM), which integrates convolutional layers with different receptive fields and central difference convolution to effectively improve boundary segmentation accuracy. In the decoder stage, we propose a confidence feature fusion module (CFFM), which incorporates deep saliency information to guide shallow features. This enables the network to focus on critical features in the target regions while suppressing background noise, resulting in a more complete target shape. Experimental results demonstrate that the proposed EANet significantly improves detection accuracy and segmentation performance for infrared small targets under various complex scenarios. It performs exceptionally well for targets of different scales and under low signal-to-noise ratio conditions.