Zhiping Wang

The detection of infrared small objects from uncrewed aerial vehicles (UAV) is critical for a wide range of Internet of Things (IoT) applications, including reconnaissance, surveillance, and security monitoring. However, existing methods for small-object detection are primarily designed for visible light images and exhibit poor performance when applied to infrared images due to their distinct characteristics, such as lower resolution, lack of color and texture information, and higher noise levels. Most existing infrared small-object detection algorithms are based on segmentation networks, which often struggle with false alarms when processing UAV-captured imagery with complex backgrounds. Moreover, these segmentation networks are computationally intensive, making them unsuitable for deployment on IoT edge devices. To address these challenges, we propose MFF-DCNet, an efficient network specifically designed for infrared small-object detection in UAV. The proposed network comprises a novel depth-wise cross-stage Transformer (DCFormer)-enhanced backbone and a multifeature focus (MFF) neck structure, collectively strengthening multiscale feature extraction and representation. Evaluations on the HIT-UAV and DroneVehicle dataset demonstrate that the proposed network achieves state-of-the-art performance with an $\text {AP}_{50-95}$ of 57.4%, representing a 5.8% improvement over specific UAV imagery detectors while simultaneously achieving a 10% increase in FPS. Furthermore, our method achieves real-time performance of 39.6 FPS on the NVIDIA Jetson Orin NX, demonstrating its practical deployment capability in resource-constrained IoT environments.