IR-DETR: An Infrared Small Object Detector Combining Edge-Aware Mechanism and Multiscale Feature Fusion

Infrared target detection plays a critical role in military reconnaissance, forest-fire prevention, and search-and-rescue operations, owing to its all-weather capability and strong penetration performance. Nevertheless, detecting ultra-small infrared targets in remote sensing and monitoring scenarios remains exceedingly challenging due to the targets' diminutive size and the long standoff distances involved. In this work, we address this problem by introducingIR-DETR—a lightweight, high-precision infrared small-target detection model built upon the real-time detection Transformer framework (RT-DETR). First, we propose the multi-scale edge-aware convolution module (LDConv), which integrates parallel multi-scale Laplacian-of-Gaussian filtering and dilated convolutions within the backbone's shallow layers, augmented by lightweight channel attention, to markedly enhance the extraction of weak texture features and boost target–background contrast. Second, we devise the MSCShiftCSP multi-scale fusion module: by orchestrating parallel multi-scale convolutional branches and parameter-free channel shifting within a CSP structure—in lieu of the standard RepC3 unit—we strengthen spatial–channel interactions and global context fusion while preserving model efficiency. Third, we replace the original P5 large-target detection head with a high-resolution P2 branch head, halving the channel count from 256 to 128 to capture edges and fine details of targets as small as 6–7 pixels while substantially reducing model complexity. Extensive ablation studies on four public datasets (IRSTD-1 K, SIRST-UAVB, SIRST-v1, and NUDT-SIRST) show that IR-DETR achieves up to a 22% increase in mAP$_{50\text{-}95}$over the RT-DETR baseline with only 9.5 million parameters; notably, on SIRST-UAVB (average target size 6-7pixels), mAP$_{75}$improves by 47%. Compared against 23 mainstream detectors—including Faster R-CNN, SSD, YOLOv5/8/10/11, and various DETR variants—IR-DETR consistently attains the highest detection accuracy across benchmarks. In summary, IR-DETR delivers a powerful yet lightweight solution for real-time detection of diverse small infrared targets in complex environments, achieving superior accuracy without significant parameter overhead and advancing the state of the art in infrared small-target detection.