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RADFRNet: Detail-Enhanced Feature Recalibration for Infrared Small-Target Detection Based on an Improved YOLOv11
Chenyang Li1, Jie Cao1,2,3, Qun Hao1
1School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
RADFRNet enhances infrared small-target detection by integrating DEConv and Adaptive Feature Recalibration (AFRE) blocks into a YOLOv11-n framework. This approach improves accuracy on challenging datasets like BIT-SIRST and FLIR-ADAS-v2.
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
Background:
- Infrared small-target detection is difficult due to low pixel occupancy, weak textures, and background clutter.
- Existing methods struggle with detail loss in backbones and semantic-spatial mismatches during feature fusion.
Purpose of the Study:
- To develop an improved detector, RADFRNet, addressing information degradation in infrared small-target detection.
- To enhance feature fusion and detail preservation for more accurate target identification.
Main Methods:
- Introduced C3DEConv by embedding the DEConv operator into C3K2 stages for detector-oriented integration.
- Developed an Adaptive Feature Recalibration (AFRE) block using Recalibration Attention Units for bidirectional feature interaction.
- Constructed four-class bounding-box annotations for the BIT-SIRST dataset.
Main Results:
- RADFRNet achieved mAP@0.5 scores of 93.2% on BIT-SIRST and 65.3% on FLIR-ADAS-v2, outperforming YOLOv11-n by 4.4% and 8.5%, respectively.
- Inference latency increased to 6.7 ms (BIT-SIRST) and 7.8 ms (FLIR-ADAS-v2) at 640x640 resolution, maintaining high FPS (149 and 128).
- Model complexity values are 8.2 M and 8.6 M.
Conclusions:
- RADFRNet significantly improves infrared small-target detection accuracy.
- The gains in accuracy come with a computational cost, positioning RADFRNet as an accuracy-oriented detector.
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