Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection
IEEE Geoscience and Remote Sensing Letters(2023)
Abstract
Object detection on very high resolution (VHR) remote sensing images plays an indispensable role in many applications. However, the large-scale target variation of remote sensing presents a significant challenge to high-precision VHR remote sensing target detection. Although existing methods attempt to enhance the feature pyramid structure and utilize various attention modules to improve the accuracy of high-resolution remote sensing object detection, small objects can still be overlooked due to the loss of critical detail features. There remain ample opportunities for improvement in multi-scale feature fusion and balance. To address this issue, this paper proposes two novel cross-layer modules: Guided Attention and Tucker Bilinear Attention. The former effectively preserves crucial detailed features, while the latter further enhances features by reasoning about semantic-level correlations. Based on these two modules, a new multi-scale remote sensing object detection framework is introduced. Compared to state-of-the-art methods on DOTA, DIOR, and NWPU VHR-10 datasets, the framework achieves comprehensive average detection accuracy for full-scale objects, and significantly enhances the detection accuracy for small objects. Code and models are available at https://github.com/Shinichict/GTNet.
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Key words
object detection,optical remote sensing imagery,bilinear poolingTucker,decomposition,self-attention
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