Accurate and robust drone detection is an important and challenging task. However, on this issue, previous research, whether based on appearance or motion features, has not yet provided a satisfactory solution, especially under a complex background. To this end, the present work proposes a motion-based method termed the Multi-Scale Space Kinematic detection method (MUSAK). It fully leverages the motion patterns by extracting 3D, pseudo 3D and 2D kinematic parameters at three scale spaces according to the keypoints quality and builds three Gated Recurrent Unit (GRU)-based detection branches for drone recognition. The MUSAK method is evaluated on a hybrid dataset named multiscale UAV dataset (MUD), consisting of public datasets and self-collected data with motion labels. The experimental results show that MUSAK improves the performance by a large margin, a 95% increase in average precision (AP), compared with the previous state-of-the-art (SOTA) motion-based methods, and the hybrid MUSAK method, which integrates with the appearance-based method Faster Region-based Convolutional Neural Network (Faster R-CNN), achieves a new SOTA performance on AP metrics (AP, APM, and APS).
Deep convolutional neural network (CNN) has made impressive achievements in the field of image restoration. However, most of deep CNN-based models have limited capability of utilizing the hierarchical features and these features are often treated equally, thus restricting the restoration performance. To address this issue, the present work proposes a novel memory-based latent attention network (MLANet) aiming to effectively restore a high-quality image from a corresponding low-quality one. The key idea of this work is to employ a memory-based latent attention block (MLAB), which is stacked in MLANet and makes better use of global and local features through the network. Specifically, the MLAB contains a main branch and a latent branch. The former is used to extract local multi-level features, and the latter preserves global information by the structure within a latent design. Furthermore, a multi-kernel attention module is incorporated into the latent branch to adaptively learn more effective features with mixed attention. To validate the effectiveness and generalization ability, MLANet is evaluated on three representative image restoration tasks: image super-resolution, image denoising, and image compression artifact reduction. Experimental results show that MLANet performs better than the state-of-the-art methods on all the tasks. INDEX TERMS Image restoration, deep learning, deep memory-based network, latent attention block.
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