DFS-YOLO: A Dynamic Feature Collaboration and State Space Framework for UAV-Based Infrared Object Detection.
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| Title: | DFS-YOLO: A Dynamic Feature Collaboration and State Space Framework for UAV-Based Infrared Object Detection. |
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| Authors: | Wang, Ziyan1,2 (AUTHOR), Li, Wangbin1,2 (AUTHOR) liwangbin@neuq.edu.cn, Sun, Kaimin3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1422. 26p. |
| Subjects: | State-space methods, Aerial surveillance, Object recognition (Computer vision), Thermal noise |
| Abstract: | Highlights: What are the main findings? The proposed DFS-YOLO framework effectively addresses low signal-to-noise ratios and severe scale variations in UAV infrared imagery through dynamic feature calibration and efficient state-space modeling. Extensive evaluations demonstrate that DFS-YOLO significantly outperforms the YOLOv12 baseline, achieving substantial precision gains on both the HIT-UAV and IRSTD-1k benchmarks. What are the implications of the main findings? Integrating linear-complexity global reasoning with an alignment-first scale fusion strategy provides an efficient method to resolve semantic misalignment and thermal clutter in weak-target detection. The model's enhanced robustness under degraded imaging conditions makes it well-suited for practical applications such as aerial surveillance and autonomous monitoring. UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO's performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The proposed DFS-YOLO framework effectively addresses low signal-to-noise ratios and severe scale variations in UAV infrared imagery through dynamic feature calibration and efficient state-space modeling. Extensive evaluations demonstrate that DFS-YOLO significantly outperforms the YOLOv12 baseline, achieving substantial precision gains on both the HIT-UAV and IRSTD-1k benchmarks. What are the implications of the main findings? Integrating linear-complexity global reasoning with an alignment-first scale fusion strategy provides an efficient method to resolve semantic misalignment and thermal clutter in weak-target detection. The model's enhanced robustness under degraded imaging conditions makes it well-suited for practical applications such as aerial surveillance and autonomous monitoring. UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO's performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091422 |
