A Dedicated Lightweight Network with Synergistic Attention for Precise Air-to-Air UAV Detection.
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| Title: | A Dedicated Lightweight Network with Synergistic Attention for Precise Air-to-Air UAV Detection. |
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| Authors: | Han, Xinheng1 (AUTHOR), Zhang, Haoyuan1,2 (AUTHOR), Wang, Jiacheng1 (AUTHOR), Xu, Jielei1,2 (AUTHOR), Lv, Yingjie1 (AUTHOR), Zhou, Zunning2 (AUTHOR), Feng, Xiaoxue1 (AUTHOR), Pan, Feng1 (AUTHOR) panfeng@bit.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1804. 18p. |
| Subjects: | Drone surveillance, Object recognition (Computer vision), Image enhancement (Imaging systems), Artificial neural networks, Real-time computing |
| Abstract: | Highlights: What are the main findings? The proposed A2A-YOLO model integrates the novel LECA-Conv module with GhostModulev2 and a Tiny Detection Head, achieving a superior balance between high detection accuracy on challenging air-to-air UAV benchmarks and real-time performance on edge RK3588 platform. Based on the insight that attention parameters need not be directly applied to original features, we design the LECA-Conv module to enable efficient local enhancement and channel-wise highlighting, thus significantly improving feature extraction for illumination-variant and motion-blurred objects at a low computational cost. What is the implication of the main finding? It provides a practical and efficient architectural solution for precise unmanned aerial vehicle (UAV) detection in real-world air-to-air scenarios, both in the visible and infrared spectra, directly addressing critical challenges like motion blur, illumination variations, and tiny targets under strict computational constraints for onboard deployment. The design principle of LECA-Conv offers a new, efficient paradigm for integrating attention and local feature enhancement, which could inspire the development of more lightweight and robust vision models for other edge-based applications. The rapid advancement of unmanned aerial vehicle (UAV) technology has made air-to-air UAV object detection increasingly essential. However, the model faces additional challenges including small target sizes, motion blur, illumination variations, and stringent real-time performance requirements under constrained computational resources. To address these challenges, this paper proposes A2A-YOLO, a specialized detection model that introduces LECA-Conv for local and channel feature enhancement to effectively mitigate motion blur and illumination variations while incorporating GhostModulev2 for efficient feature extraction and Tiny Detection Heads for improved small target recognition. The proposed LECA-Conv module operates on the principle that attention parameters need not directly modify original feature maps, a key insight validated through extensive experiments. Extensive evaluations on the Det-Fly dataset demonstrate A2A-YOLO's superior performance with 85.0 % precision (P P) , 80.7 % recall (P R) , and 81.9 % average precision (A P) , outperforming YOLO11 by 0.9 % , 8.4 % , and 6.5 % , respectively. The proposed method demonstrates outstanding performance across diverse backgrounds and challenging conditions including motion blur and illumination variations. The model achieves real-time detection at 15 FPS on RK3588 platform while delivering remarkable performance in infrared small target detection. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The proposed A2A-YOLO model integrates the novel LECA-Conv module with GhostModulev2 and a Tiny Detection Head, achieving a superior balance between high detection accuracy on challenging air-to-air UAV benchmarks and real-time performance on edge RK3588 platform. Based on the insight that attention parameters need not be directly applied to original features, we design the LECA-Conv module to enable efficient local enhancement and channel-wise highlighting, thus significantly improving feature extraction for illumination-variant and motion-blurred objects at a low computational cost. What is the implication of the main finding? It provides a practical and efficient architectural solution for precise unmanned aerial vehicle (UAV) detection in real-world air-to-air scenarios, both in the visible and infrared spectra, directly addressing critical challenges like motion blur, illumination variations, and tiny targets under strict computational constraints for onboard deployment. The design principle of LECA-Conv offers a new, efficient paradigm for integrating attention and local feature enhancement, which could inspire the development of more lightweight and robust vision models for other edge-based applications. The rapid advancement of unmanned aerial vehicle (UAV) technology has made air-to-air UAV object detection increasingly essential. However, the model faces additional challenges including small target sizes, motion blur, illumination variations, and stringent real-time performance requirements under constrained computational resources. To address these challenges, this paper proposes A2A-YOLO, a specialized detection model that introduces LECA-Conv for local and channel feature enhancement to effectively mitigate motion blur and illumination variations while incorporating GhostModulev2 for efficient feature extraction and Tiny Detection Heads for improved small target recognition. The proposed LECA-Conv module operates on the principle that attention parameters need not directly modify original feature maps, a key insight validated through extensive experiments. Extensive evaluations on the Det-Fly dataset demonstrate A2A-YOLO's superior performance with 85.0 % precision (P P) , 80.7 % recall (P R) , and 81.9 % average precision (A P) , outperforming YOLO11 by 0.9 % , 8.4 % , and 6.5 % , respectively. The proposed method demonstrates outstanding performance across diverse backgrounds and challenging conditions including motion blur and illumination variations. The model achieves real-time detection at 15 FPS on RK3588 platform while delivering remarkable performance in infrared small target detection. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111804 |
