# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 1 Introduction  
Infrared small-target detection is pivotal for applications ranging from military reconnaissance and precision-guided munitions to maritime search-and-rescue and environmental monitoring. In these scenarios, targets often occupy only a handful of pixels against cluttered, low-contrast backgrounds, making them extremely difficult to discern from noise. A missed detection can translate to mission failure or loss of life, while false alarms waste critical resources. Moreover, infrared imaging's all-weather capability and effectiveness in low-light conditions underscore its strategic value, yet these same advantages amplify the challenge of reliably identifying minute thermal signatures under varying environmental conditions.  
Traditional infrared small-target detectors--whether based on handcrafted spatial filters or on early adaptations of general object-detection CNNs--struggle when targets occupy only a few pixels amid heavy clutter or low contrast. Hand-designed filters require careful parameter tuning and tend to produce excessive false alarms under complex backgrounds, while naive CNN adaptations lack the dense supervision and fine-grained feature preservation needed for sub-pixel localization. Even recent specialized deep models, despite improvements in overall recall and precision, still struggle on two fronts.  
To bridge this gap, we introduce PSHNet, a hybrid deep-learning framework that unifies dense spatial supervision with precise geometric refinement.  
1. PSHNet addresses the lack of pixel-level guidance under sparse box-level supervision by generating a position heatmap, where each target center is encoded as a Gaussian distribution to supply dense localization cues. A dedicated offset branch then regresses sub-pixel shifts to refine the coarse heatmap peaks, while a size branch directly predicts each target's width and height, yielding precise position-scale estimates.
2. To counteract the loss of fine details caused by deep-layer downsampling, we incorporate an Enhanced Low-level Feature Module (ELFM) immediately after the first encoder block. ELFM applies multi-scale dilated convolutions to capture contextual information across varying receptive fields and employs channel-wise attention to emphasize channels most relevant to small targets, thus preserving and amplifying critical shallow-layer spatial features.
3. To compensate for the limited global context of heatmap-plus-regression alone, we introduce a CIoU regularization term on the reconstructed bounding boxes. This auxiliary loss enforces consistency in location, scale, and aspect ratio without overriding the dense supervision signals, ensuring that detections remain both spatially sensitive and geometrically aligned.  
Through the tight integration of dense heatmap cues, geometry-aware regression, and enhanced low-level feature preservation, PSHNet reliably achieves high recall and precision in detecting minute infrared targets under complex backgrounds and low-signal conditions. To further clarify the motivation behind our design, a detailed discussion of existing methods and their limitations is presented in the next section.