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Differentiated Attention Guided Network Over Hierarchical and Aggregated Features for Intelligent UAV Surveillance
Houzhang Fang, Zikai Liao, Xuhua Wang, Yi Chang, and Luxin Yan
Intelligent unmanned aerial vehicle (UAV) surveillance based on infrared imaging has wide applications in the anti-UAV system for protecting urban security and aerial safety. However, weak target features and complex background distraction pose great challenges for the accurate detection of UAVs. To address this issue, we propose a novel differentiated attention guided network to adaptively strengthen the discriminative features between UAV targets and complex background. First, a novel spatialaware channel attention (SCA) is introduced into deep layers via preserving critical spatial features and leveraging channel interdependencies to focus on the large-scale targets. The channel-modulated deformable spatial attention is introduced into shallow layers via refining channel context and dynamically perceiving the spatial features for focusing on the small-scale targets. A combination of the above two attention mechanisms is employed in intermediate layers of the network for concentrating on the mediumscale targets. Then, we embed a feature aggregator at the detection branches to guide the information exchange of high-level feature maps and low-level feature maps with a bottom-up context modulation, and integrate an SCA at the end to further boost the distinctive feature representation for task-awareness. The above design can adaptively enhance multiscale UAV target features and suppress complex background interferences, leading to better detection performance, especially for small targets. Extensive experiments on real infrared UAV datasets reveal that the proposed method outperforms the baseline object detectors by a large margin, validating its feasibility in real-world infrared UAV detection. The source code can be found at https://github.com/KALEIDOSCOPEIP/DAGNet.
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