# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 2 Related Work
### Deep Learning-Based Infrared Small-Target Detection Methods  
In recent years, deep learning has significantly advanced infrared small-target detection by leveraging data-driven feature extraction and end-to-end optimization. Compared with traditional handcrafted methods, deep models offer greater adaptability to cluttered scenes and complex environmental variations. Existing approaches can be broadly categorized based on their architectural foundations and learning strategies.  
(1) Detection-Based Frameworks (Box-Level Supervision):  
Several studies have adapted mainstream object detectors such as Faster R-CNN and YOLO to infrared data. For example, IAA-Net employs a two-stage pipeline with a ResNet-18-based region proposal module and a cross-channel attention mechanism to refine target localization [9]. YOLO-FR enhances YOLOv5 with a feature reassembly module to improve small target saliency [10], while YOLOSR-IST combines super-resolution preprocessing with a lightweight YOLO detection head to increase recall under low SNR conditions [11]. Anchor-free variants, such as ISTD-CenterNet, utilize keypoint-based regression to estimate target centers and sizes without predefined anchor boxes, offering improved adaptability for irregular or sub-pixel targets [12].  
(2) Segmentation-Style Networks (Pixel-Level Supervision):  
Many recent approaches adopt encoder-decoder architectures to produce pixel-level predictions of small target regions. For instance, MDvsFA introduces a dual-branch adversarial framework that balances missed detections (MD) and false alarms (FA) through class-specific discriminators [13]. ALCNet incorporates a local contrast prior into a U-shaped network and applies bottom-up attention to retain fine spatial features [14]. DNANet features a densely nested attention structure that enhances multi-scale fusion and suppresses redundant noise, outperforming many anchor-based methods [15]. Other segmentation-style methods include MSHNet and ISNet, which introduce hierarchical skip-connections or shape-guided supervision to recover target geometry and location [16; 17].  
(3) Transformer-Based Models:  
Recently, Transformer architectures have been applied to enhance global feature modeling in infrared detection tasks. FTC-Net integrates CNN and Transformer branches to combine local spatial precision with long-range contextual cues, improving robustness in cluttered backgrounds [18]. TCI-Former further introduces a thermal-conduction-inspired module to encode spatial propagation dynamics of thermal signals, aiding weak target discrimination [19].  
(4) Specialized Training Mechanisms:  
Adversarial training is exemplified by MDvsFA [13], while self-supervised learning (SSL) has been explored in SSL-YOLO, where instance discrimination pretraining improves target generalization with limited labeled infrared data [20]. Point-supervised methods, like PSMNet, reduce annotation effort by using only center points as labels and employing uncertainty modeling to guide detection [21].  
(5) Multimodal and Data-Augmented Techniques:  
Methods such as ESM-YOLO fuse visible and infrared inputs via dual-branch encoders and attention-based feature fusion, enabling improved cross-modality saliency [22]. Synthetic infrared datasets and targeted data augmentation strategies (e.g., simulated injection, clutter modeling) have also been proposed to enhance generalization under limited data regimes [23].  
In summary, deep learning has enabled substantial progress in infrared small-target detection.