What question did this study set out to answer?

The research aims to improve the infrared detection of small unmanned aerial vehicles (UAVs) in various weather conditions.

April 15, 2026

SIM-AIR Dataset and YOLO-KMM Model for Air-to-Air Infrared Small Target Detection

Puntos clave

The research aims to improve the infrared detection of small unmanned aerial vehicles (UAVs) in various weather conditions.
Constructed the SIM-AIR dataset with 3,993 annotated images across four weather conditions.
Developed YOLO-KMM, an object detection model based on YOLOv11, for small target characteristics.
Integrated the C2KD feature enhancement module and C3K2-MU detection head for better performance.
Achieved mAP₅₀ of 88.2%, outperforming baseline YOLOv11 by 7.8 percentage points.
Demonstrated high precision of 94.0% and recall of 74.3%.
Reduced small-target missed detection rate by 12.5%.
Maintained fast inference speed of 246.18 FPS with low computational requirements.

Resumen

To address the lack of dedicated datasets for infrared detection of small UAVs in air-to-air scenarios, this paper first constructs the self-built SIM-AIR dataset covering complex scenarios, and then proposes YOLO-KMM an efficient YOLOv11-based object detection model tailored to the dataset's small-target characteristics and deployment requirements; collected by an UAV equipped with an infrared thermal imager, the SIM-AIR dataset consists of 3,993 precisely annotated images across four weather conditions: sunny, cloudy, snowy, and hazy, where 99.7% of the targets are ultra-small objects and their width < 40 pixels, with an average size of 11.2×6.6 pixels, including complex scenarios such as "dark targets" in snowy weather and low signal-to-noise ratio (SNR) in haze, which fully simulate real-world detection challenges. To tackle the issues of sparse small-target features and strong background interference, YOLO-KMM integrates the C2KD feature enhancement module and C3K2-MU lightweight detection head, forming a dual-optimized architecture of "feature enhancement - efficient detection": the C2KD module captures weak small-target features and suppresses noise via cross-scale fusion and attention mechanisms, while the C3K2-MU module adopts grouped convolution and depthwise separable convolution to reduce the number of parameters while preserving feature representation capability. Experiments on the SIM-AIR dataset show that YOLO-KMM achieves an mAP₅₀ of 88.2%. This is 7.8 percentage points higher than the baseline YOLOv11, with a precision of 94.0% and recall of 74.3%, reduces the small-target missed detection rate by 12.5%, and maintains an inference speed of 246.18 FPS, 2.3M parameters, and 5.4 GFLOPs of computation; compared with YOLOv5/8/12, the model achieves a better balance among accuracy, speed, and complexity, verifying the practicality and challenge of the SIM-AIR dataset and providing an efficient solution for air-to-air small-target infrared detection.

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