A Lightweight Model for UAV-Based Infrared Small Object Detection

Abstract

Detecting small infrared targets from Uncrewed Aerial Vehicles (UAVs) at night presents a significant challenge, requiring a delicate balance between high accuracy and low computational cost for resource-constrained edge deployment. To address this, we introduce a lightweight, high-precision framework extending the YOLOv11 architecture. First, a DP-PSA block, integrating Progressive Channel-wise Self-Attention (PCSA) and Dynamic Tanh (D-Tanh), is proposed to amplify small-object features within the C2PSA module. Second, a Bottleneck-FPD module, combining Frequency Dynamic Convolution (FDConv) with Partial Convolution (PConv), is designed to drastically cut computational redundancy while preserving feature richness. Finally, coupled with the Lightweight Single Channel Detection (LSCD) head, our model surpasses the YOLOv11 baseline. Experimental results demonstrate that it achieves an mAP@[.50:.95] of 52.0% and an inference speed of 382 FPS, while simultaneously reducing GFLOPs by 27% and parameters by 23%. This work provides a practical and efficient solution for real-time aerial surveillance at night.