RADFRNet: Detail-Enhanced Feature Recalibration for Infrared Small-Target Detection Based on an Improved YOLOv11

Infrared multi-class small-target detection is challenging because targets occupy few pixels, exhibit weak texture, and are easily confused with background clutter. We present RADFRNet, a YOLOv11-n-based detector designed to address two forms of information degradation: detail loss in the backbone and semantic–spatial mismatch during cross-level feature fusion. First, the previously proposed DEConv operator is embedded into selected C3K2 stages to form C3DEConv; the contribution lies in its C3K2-compatible, detector-oriented integration rather than in a new differential-convolution formulation. Second, an Adaptive Feature Recalibration (AFRE) block constructed from three Recalibration Attention Units performs bidirectional interaction between shallow spatial details and deep semantic features. We also construct four-class bounding-box annotations for BIT-SIRST. RADFRNet achieves mAP@0.5 scores of 93.2% and 65.3% on BIT-SIRST and FLIR-ADAS-v2, improving YOLOv11-n by 4.4 and 8.5 percentage points, respectively. Under the same original (Formula presented.) GPU inference setup, the per-image latency increases from 3.3 to 6.7 ms on BIT-SIRST and from 3.0 to 7.8 ms on FLIR-ADAS-v2, corresponding to nominal throughputs of approximately 149 and 128 FPS for RADFRNet. The reported model-complexity values are 8.2 M and 8.6 M, respectively. These results show that RADFRNet retains high-rate GPU inference, although the accuracy gains are obtained at a clear computational cost; the model is therefore positioned as an accuracy-oriented detector rather than a latency-neutral replacement for YOLOv11-n.