To improve the detection performance of aircraft swarms in remote sensing images with characteristics of small target, scale diversity and dense distribution, a multi-scale-feature-fused attention mechanism is proposed and used for deep learning networks in this paper. Based on the fundamental YOLOv5 network, an enhanced multi-scale CBAM attention module that combines the channel attention and the spatial attention is performed on the fused feature maps at various stages and scales. Consequently, more detailed attention information can be obtained. Experimental results demonstrate that the proposed method can effectively improve the detection accuracy of aircraft swarm targets compared with some traditional methods. In detail, the proposed method can reach 9.2% higher recall than the original YOLOv5 and 3.4% higher recall than the YOLOv5 integrated with traditional CBAM modules while the computational cost is similar
In this paper, a multi-feature fusion probabilistic topic model, called MFF-PTM, is proposed to realize unsupervised 3D point cloud classification. Our MFF-PTM consists of three key stages: 1) a novel multi-feature descriptor is designed to characterize different 3D point clouds by the combination of statistical, morphological and histogram features; 2) a Rsphere clustering algorithm is proposed to construct 3D visual vocabulary and generate the co-occurrence matrix, which can effectively avoid the initialization problem of category; 3) PTM employs the co-occurrence matrix to predict the probability distribution of a certain point cloud belonging to different category topics. The experimental results have clearly shown that the proposed MFF-PTM model can outperform the traditional PTM models with single feature description for 3D point cloud classification.
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