Fig 1.
IR UAV Detection Trilemma.
Table 1.
Related datasets.
Table 2.
YOLO-Based Visual UAV Detection Methods.
Fig 2.
Infrared UAV Dataset (AUVD-Seg300) and Segmentation Annotations.
Fig 3.
Detailed technical architecture of YOLO11-AU-IR.
Table 3.
Module replacement computational cost analysis.
Table 4.
Hardware and software platform configurations.
Table 5.
a. Comparative results of lightweight models (≤15MB) on AUVD-Seg300 dataset. b. Comparative results of medium and heavyweight models (>15MB) on AUVD-Seg300 dataset.
Fig 4.
Simplified flow diagram highlighting the integration of our key contributions (EADown, HSAN, ATFL) within the detection pipeline.
Fig 5.
Workflow of YOLO11-AU-IR model development and testing.
Fig 6.
Input images and segmentation results of various models on the AUVD-Seg300 dataset.
Fig 7.
Model efficiency and performance trade-off analysis.
(a) Relationship between mAP@0.5 and inference time; (b) Relationship between mAP@0.5: 0.95 and inference time; (c) Relationship between mAP@0.5 and GFLOPs; (d) Relationship between mAP@0.5 and the number of parameters.
Fig 8.
Comparison of training performance of different models on the AUVD-Seg300 dataset.
(a) Accuracy curve; (b) Recall curve; (c) mAP@0.50 curve; (d) mAP@0.50:0.95 curve.
Table 6.
Comparison of experimental results for multi-scale object detection.
Table 7.
Three-fold cross-validation performance comparison.
Fig 9.
Normalized confusion matrix for YOLO11-AU-IR showing classification performance across UAV categories.
Fig 10.
Error-focused Grad-CAM comparison.
Table 8.
Ablation study results.
Fig 11.
Bar chart visualization of ablation study results showing progressive performance improvements with each module integration.
Fig 12.
Training performance comparison across ablation configurations.
(a) Baseline, (b) Baseline+EADown, (c) Baseline+HSAN, (d) Baseline+EADown+HSAN, (e) YOLO11-AU-IR. Each subplot shows training/validation loss curves and four key metrics: precision, recall, mAP@0.50, and mAP@0.50:0.95.
Fig 13.
Inference testing on the NVIDIA Jetson TX2 platform.
Fig 14.
Computational performance comparison among Jetson edge devices.
Table 9.
Model performance comparison for ONNX (INT8) deployment.
Table 10.
Model performance comparison for torchscript (optimize, INT8) deployment.
Table 11.
Module-wise resource consumption analysis on NVIDIA Jetson TX2.