To solve the problem of adhesion objects and data distribution deviations in few-shot scenarios, a synthetic aperture radar (SAR) object detection method based on meta-learning is proposed, which includes support feature guidance block and variational inference block. The former enhances the key features used for bounding box positioning in the query feature, so that the module can generate accurate proposals even in face of the adherent SAR objects. On this basis, to correct the deviation of the data distribution caused by the few-shot data, a variational inference block is constructed to map the supporting features to the class distribution in the hidden space. To fuse robust class-level features, meta-knowledge is used to calculate the distribution of the support feature classes of classes. The proposed algorithm uses a few-shot support set data to migrate priori knowledge to a class using the few-shot tasks and data double sampling. Moreover, a few-shot SAR object detection dataset is established to verify the effectiveness of the proposed method, and the experimental results show that our method has obvious advantages over the representative few-shot SAR object detection algorithms.
With the development of synthetic aperture radar (SAR) technology, more SAR datasets with high resolution and large scale have been obtained. Research using SAR images to detect and monitor marine targets has become one of the most important marine applications. In recent years, deep learning has been widely applied to target detection. However, it was difficult to use deep learning to train an SAR ship detection model in complex scenes. To resolve this problem, an SAR ship detection method combining YOLOv4 and the receptive field block (CY-RFB) was proposed in this paper. Extensive experimental results on the SAR-Ship-Dataset and SSDD datasets demonstrated that the proposed method had achieved supreme detection performance compared to the state-of-the-art ship detection methods in complex scenes, whether they were in offshore or inshore scenes of SAR images.
Motion trajectory recognition is one of the most important means to determine the identity of a moving object. A compact and discriminative feature representation method can improve the trajectory recognition accuracy. This paper presents an efficient framework for action recognition using a three-dimensional skeleton kinematic joint model. First, we put forward a rotation-scale-translation-invariant shape descriptor based on point context (PC) and the normal vector of hypersurface to jointly characterize local motion and shape information. Meanwhile, an algorithm for extracting the key trajectory based on the confidence coefficient is proposed to reduce the randomness and computational complexity. Second, to decrease the eigenvalue decomposition time complexity, a tensor shape descriptor (TSD) based on PC that can globally capture the spatial layout and temporal order to preserve the spatial information of each frame is proposed. Then, a multilinear projection process is achieved by tensor dynamic time warping to map the TSD to a low-dimensional tensor subspace of the same size. Experimental results show that the proposed shape descriptor is effective and feasible, and the proposed approach obtains considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.
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