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Multi-scale object detection by bottom-up feature pyramid network

Multi-scale object detection by bottom-up feature pyramid ne...

引用

IET International Radar Conference 2018, IRC 2018

作者： Boya, Zhao Baojun, Zhao Linbo, Tang Chen, Wu Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China

The deep neural networks has been developed fast and shown great successes in many significant fields, such as smart surveillance, self-driving and face recognition. The detection of the object with multi-scale and multi-aspect-ratio is still the key problem. In this study, the authors propose a bottom-up feature pyramid network, coordinating with multi-scale feature representation and multi-aspect-ratio anchor generation. Firstly, the multi-scale feature representation is formed by a set of fully convolutional layers which is catenated after the backbone network. Secondly, in order to link the multi-scale feature, the deconvolutional layer is involving after each multi-scale feature map. Thirdly, to tackle the problem of adopting object with different aspect ratios, the anchors on each multi-scale feature map are generated by six shapes. The proposed method is evaluated on PASCAL visual object detection dataset and reach the accuracy of 80.5%. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Aspect ratio

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Robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression

Robust knowledge-aided sparse recovery STAP method for non-h...

引用

IET International Radar Conference 2018, IRC 2018

作者： Peng, Hao Sun, Yuze Yang, Xiaopeng Yang, Jian Beijing China Department of Electronic Engineering Tsinghua University Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronic Beijing Institute of Technology Beijing China

Conventional space-time adaptive processing (STAP) methods would suffer severely performance loss in the complex clutter environment of an airborne-phased array radar, especially when the estimated clutter covariance matrix (CCM) is corrupted by the interference targets (outliers). In order to improve the clutter suppression in a practical complex clutter, a robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression is proposed. In the proposed method, the spectral profiles of the clutter and outliers are firstly estimated by sparse recovery processing. Then based on the system prior parameters, the clutter mask is constructed to select the space-time steering vectors corresponding to the clutter components. Afterwards the clutter suppression is achieved based on the clutter subspace obtained from the selected space-time steering vectors. Since the clutter and outlier profiles are effectively estimated and distinguished by the knowledge-aided sparse recovery processing, robust clutter subspace estimation can be achieved for clutter suppression. Through the simulated and actual airborne-phased array radar data, it is verified that the proposed method can effectively improve the STAP performance in a non-homogeneous environment. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Space time adaptive processing

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Radar data simulation using deep generative networks

Radar data simulation using deep generative networks

引用

IET International Radar Conference 2018, IRC 2018

作者： Song, Yiheng Wang, Yanhua Li, Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China

Due to the high cost of real experiments, radar data simulation plays an important role in radar applications. However, the accuracy and the calculation speed of existing simulation methods is limited by the model error and the heavy calculation of electromagnetic simulation. Here, a radar data simulation method based on deep generative network (DGN) is proposed. DGN is generative model involving deep network as the representation tool, in which the model is trained with labelled data. When the training phase is finished, the generative model can generate data samples which are similar to the real samples. The performance of the DGN is evaluated on the ground-based radar dataset, and the results show that the distribution of the generated radar data is similar to the training radar data. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Electromagnetic simulation

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SAR Parameter Estimation Method for Rectangle Plane Based on information Geometry

SAR Parameter Estimation Method for Rectangle Plane Based on...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Yuhan Wen Xinliang Chen Yangkai Wei Yujie Fan Tao Zeng Zegang Ding Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

After establishing scattering models, the traditional synthetic aperture radar (SAR) parameter estimation methods usually utilize vanilla gradients to estimate target parameters from SAR echoes or images. However, the vanilla gradients will lead to lower iteration efficiency. Hence, in this paper, to take the rectangle plane as an example, we propose a SAR parameter estimation method based on the information geometry (SPEM-IG) to solve the above problem. The SPEM-IG adopts the natural gradient to estimate the parameters based on the information geometry optimization algorithm. The central objective of SPEM-IG is to project the least square function into probability distribution space and find the optimal solution in the new parameter space. In order to reduce the computational complexity caused by the multi-peak feature of least squares function, SPEM-IG utilizes the range of parameters rather than its exact value in calculating the value of least squares function, and takes the optimal solution in this range as the result of the current iteration process. By considering the geometric structure of manifolds, SPEM-IG calculates the optimal gradient direction called natural gradient to obtain faster convergence rate. The experimental results show that the SPEM-IG is effective for estimating target parameters. Besides, SPEM-IG has good performance even at low signal-to-noise ratios of SAR images.

关键词： parametric scattering model information geometry natural gradient

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Research on Infrared And Vsisible Image Fusion System

Research on Infrared And Vsisible Image Fusion System

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Xinxin He Linbo Tang Chen Wu Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

Infrared and visible image fusion is an important branch of image fusion technology. By combining the image information of two different bands, the human eye or machine can detect the target more comprehensively, quickly and accurately, and image fusion can improve the ability of human vision to understand the scene, which has great application value in military and civilian fields. Therefore, the research of infrared and visible image fusion systems has important theoretical and practical significance. In this paper, the author takes infrared and visible images as the research object, expounds the basic theory of image fusion, designs the fusion algorithm and the system based on FPGA + ARM, and also gives the complete solution of image fusion system. The experimental results show that the image fusion system is built successfully, the system image fusion effect is good, and the real-time processing is realized. After further experiment and design optimization, the system can be applied in engineering.

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High-efficiency scene classification based on deep compressed-domain feature

High-efficiency scene classification based on deep compresse...

引用

IET International Radar Conference 2018, IRC 2018

作者： Li, Cheng Zhao, Baojun Zhao, Boya Wang, Wenzheng Duan, Chenhui Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key laboratory of Embedded Real-time Information Processing Technology Beijing Institute of technology Beijing100081 China

Remote sensing image (RSI) scene classification has become a more and more fundamental issue in satellite and UAV time-sensitive applications. However, as the volume and velocity of RSIs are undergoing an explosive growth, traditional effective technologies claim a huge amount of computing resources, on a scale that already goes beyond in-orbit processing capacity. Here, we attempt to design a deep feature representation framework based on onboard compressed data to solve the aforementioned problem. Firstly, we extract header and body information in raw JPEG2000 codestream for representing geometrical and geospatial property of RSIs. Moreover, a novel compressed-domain convolutional neural network (CNN) is proposed to obtain high-level representation for effective classification. Extensive experiments demonstrate that, in comparison with the existing relevant state-of-the-art approaches, the proposed method achieves high classification accuracy with faster computation and lower consumption. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Orbits

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Spaceborne synthetic aperture radar imaging mapping methodology based on FPGA-DSP hybrid heterogeneous architecture

Spaceborne synthetic aperture radar imaging mapping methodol...

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IET International Radar Conference 2018, IRC 2018

作者： Yu, Wenyue Xie, Yizhuang Li, Bingyi Chen, He Liu, Xiaoning Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China DFH Satellite Co. Ltd. Beijing100081 China

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data bring challenges for high-speed transmission and real-time processing. The general platform with GPU, CPU or digital signal processor (DSP) cannot meet the requirement for short delay and low power. Therefore, a field-programmable gate array-digital signal processor (FPGA-DSP) SAR imaging accelerate platform has become a solution for processing performance with a lower latency and power. In this study, an effective mapping strategy is adopted to the FPGA-DSP hybrid heterogeneous architecture to accelerate the processing performance according to the analysis of Chirp Scaling SAR imaging algorithms. As a proof of concept, a FPGA-DSP hybrid heterogeneous accelerating platform is designed and realised. The platform requires 12 s to focus a stripmap SAR raw data with a granularity of 16,384 × 16,384. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Digital signal processors

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Parameter estimation of G0 distribution based on improved recursive expectation-maximisation method for clutter modelling

Parameter estimation of G0 distribution based on improved re...

引用

IET International Radar Conference 2018, IRC 2018

作者： Lu, Jiaxin Sun, Yuze Zhuo, Bangsheng Yang, Xiaopeng Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electronic Engineering Tsinghua University Beijing100084 China

Modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation-maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Maximum likelihood estimation

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Small vehicles detection based on UAV

Small vehicles detection based on UAV

引用

IET International Radar Conference 2018, IRC 2018

作者： Chen, Wu Baojun, Zhao Linbo, Tang Boya, Zhao Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China

In recent years, with the development of deep neural networks, object detection has been widely used in many fields such as video surveillance, face recognition, and unmanned aerial vehicles (UAVs). However, small objects detection is still a challenge for the most of existing methods due to its low resolution and limited information. Here, the authors use an effective object detector single shot multi-box detector (SSD) as base architecture to detect small vehicles shot by UAV platform. First, in order to improve the performance for small objects, an extended-layer feature method is proposed for introducing more small-scale information. Second, the shapes of default boxes on specific feature maps are adjusted to balance the performance loss caused by adding feature maps. Experimental results show that this method reached the accuracy of 78.6% on PASCAL VOC object-detection dataset, which has a 1.8% bonus compared with the baseline. Moreover, in order to show the effectiveness on small vehicles, an additional experiment is conducted on the UAV123 dataset, which gets a 3.1% bonus. The speed of the proposed method has a 39 fps on a NVIDIA GTX Titan X. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Object detection

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The Verification System of Sliding Spotlight Mode SAR Imaging based on SoPC

The Verification System of Sliding Spotlight Mode SAR Imagin...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Hu, Shankang Xie, Yizhuang Lian, Jie Li, Bingyi Beijing Institute of Remote Sensing Equipmentt BeiJing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Institute of Radio Metrology and Measurement Beijing China

ISBN: (纸本)9781728123455

Aiming at the application background of sliding spotlight mode Synthetic Aperture Radar (SAR) imaging processing, this paper builds a System-on-a-Programmable-Chip-based (SoPC-based) sliding spotlight mode SAR imaging verification system, which uses software and hardware collaborative design method to complete algorithm-to-hardware mapping and can quickly and truly perform in FPGA or ASIC. Finally, we analyzed the implementation of the imaging prototype system and the imaging results. For the generation of correlation factors, this paper hierarchically decomposes the factor generation operation to realize the time division multiplexing of the operation unit, in order to reduce resource consumption. At the same time, a variable point Fast Fourier Transformation (FFT) processor based on a variable radix-23 multiplexing butterfly unit is designed and implemented. Combining these two parts with other modules enables the entire sliding spotlight mode imaging process. On the basis of keeping the error of the result small, the overall resource consumption is greatly reduced. The 102432768 point FFT is implemented by the top-level configurable method, and the operation delay is reduced by more than 20% compared with the Xilinx FFT IP core. The final imaging time is reduced to seconds, and the imaging results meet the requirements. Subsequent compatibility design for other SAR imaging modes also can be achieved. © 2019 IEEE.

关键词： Synthetic aperture radar

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