KEYWORDS: 3D modeling, Data modeling, Detection and tracking algorithms, Target recognition, 3D acquisition, Convolution, 3D image processing, Databases, Computer simulations, Patents
This paper is aimed at the type recognition of aircraft, with four kinds of typical military aircraft as research objects. In this paper, we establish a database on aircraft type and propose an effective and efficient method of type recognition called Geometric-Convolutional Neutral Networks(G-CNN) in a coarse-to-fine manner. We start with target characteristics for the first time and establish a target characteristics database by analyzing the acquired characteristics such as geometric characteristics and optical characteristics. Next, aiming at the problem that the dataset on aircraft types is few, we build 3D models based on the characteristics database and make an aircraft type dataset using 3D simulation creatively, which is of great significance for the research on aircraft type recognition. Finally, we extract the geometric characteristics of the aircraft—affine invariant moments and aspect ratios, realizing a fast and efficient region selecting; we improve residual blocks with dilated convolution, which is used for type recognition for the first time. Our method achieves 89.0%mAP and the experiments show that it tackles the type recognition problems with improved performance.
The available high-resolution remote sensing images are growing exponentially in recent years due to the rapid development of remote sensing imaging. However, several problems still exist: 1) How to solve the difficulty caused by the scale and shape of object. 2) How to detect the object quickly and accurately. Inspired by the hierarchical visual perception mechanism, we propose a fusion method combining the low-level feature and high-level feature obtained by convolution neural networks to detect ship target. At the same time, we introduce deformable CNN layer into convolution neural networks to solve the diverse scale and shape of object. Finally, based on the visual attention mechanism, the object contextual information is integrated into the network. The experiment results show that our model can achieve good detection performance and the framework has good expansibility.
Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.
This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.
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