IRSTD-YOLO: An Improved YOLO Framework for Infrared Small Target Detection

Abstract

Detecting small targets in infrared images, especially in low-contrast and complex backgrounds, remains challenging. To tackle this, we propose infrared small target detection YOLO (IRSTD-YOLO), a novel detection network. The edge and feature extraction (EFE) module enhances feature representation by integrating a SobelConv branch and a 2DConv branch. The SobelConv branch applies Sobel operators to extract gradient information, enhancing edge contrast and making small targets more distinguishable from the background. Unlike standard convolutions, which process all features uniformly, this edge-aware operation emphasizes structural information crucial for detecting small infrared targets. The 2DConv branch captures spatial context, complementing the edge features to create a more comprehensive representation. To further refine detection, we introduce the infrared small target enhancement (IRSTE) module, addressing the limitations of conventional feature pyramid networks. Instead of merely adding a shallow detection head, IRSTE processes and enhances shallow-layer features, which are rich in small target information, and fuses them with deeper features. By leveraging a multibranch strategy that integrates local, global, and large-scale contexts, IRSTE enhances small target representation and detection robustness, particularly in low-contrast environments where traditional networks often fail. Experimental results show that IRSTD-YOLO achieves an mAP@0.5:0.95 of 36.7% on the InfraredUAV dataset and 51.6% on the AntiUAV310 dataset, outperforming YOLOv11-s by 4.4% and 4.2%, respectively. Code is released at https://github.com/vectorbullet/IRSTD-YOLO