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small-scale pedestrian detection method based on attention mechanism and adaptive feature fusion

Small-scale pedestrian detection method based on attention m...

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2021 International conference on Advanced Algorithms and signal Image processing, AASIP 2021

作者： You, Yuhao Chen, Houjin Li, Yanfeng Wang, Minjun Zhu, Jinlei School of Electronic Information Engineering Beijing Jiaotong University Beijing China Synthesis Electronic Technology Co. Ltd. Shandong Jinan China

In pedestrian detection, high detection accuracy has been achieved for large-scale pedestrians, but for small-scale pedestrians, the detection effect needs to be further improved. In order to improve the detection accuracy of small-scale pedestrians, this paper proposes a YOLOv4 small-scale pedestrian detection algorithm based on the fusion of attention and weighted features. In order to enhance effective features and suppress ineffective features, an attention mechanism that adapts features is introduced in the backbone network CSPdarknet53 to reduce the interference to small-scale pedestrian detection. In order to better integrate features with inconsistent semantics and scales, adaptive channel-weighted feature fusion is used in the feature pyramid, so that the deep and shallow features focus on pedestrian targets of corresponding scales. The logarithmic mean missed detection rate (LAMR) on the small-scale pedestrian test sets "Far" and "Medium" of the Caltech public data set has decreased: 9.67 % And 7.38%. Compared with other pedestrian detection algorithms, it has obvious advantages for small-scale pedestrian detection. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Semantics

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Experimental evaluation of Supervised Reciprocal Filter Strategies for OFDM-radar signal processing

Experimental evaluation of Supervised Reciprocal Filter Stra...

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IEEE National conference on Radar

作者： Javier Trujillo Rodriguez Fabiola Colone Pierfrancesco Lombardo Dept. of Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

In this paper, we present an experimental evaluation of recently proposed Supervised Reciprocal Filter approaches for the compression of OFDM-radar signals. The range-Doppler map is usually evaluated using a suboptimal batches algorithm, after fragmenting the signal in batches with length equal to the OFDM symbol. Using “OFDM fragmentation” requires symbol synchronization and sets constraints on the non-ambiguous Range-Doppler area of targets that can be detected with limited signal-to-Noise Ratio (SNR) loss. Supervised Reciprocal Filters have been recently proposed to operate with batches of longer lengths than the OFDM symbol without requiring any synchronization. In this paper we extend the study to include the case of batches equal to a fraction of the OFDM symbol, which provides higher flexibility to adapt the processor to the range-Doppler scenario of interest. These filters have been shown to contain the large SNR losses obtained with a direct application of the Reciprocal Filter (RF) with the non-OFDM fragmentation. Moreover, they have been shown theoretically to preserve the benefits of the RF over the Matched Filter (MF) against the clutter-limited scenarios. To assess the performance of the Supervised Filter against a real scenario, an acquisition campaign has been carried out using the Sapienza experimental passive radar along the coast north of Rome, against a maritime traffic scenario, including non-cooperative vessels, as well as a cooperating small boat equipped with differential GPS positioning registration tools. The effectiveness of the proposed approaches is validated by applying them to experimental data from a PBR application exploiting DVB-T transmissions.

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Dense Convolution Siamese Network for Hyperspectral Image Target Detection

Dense Convolution Siamese Network for Hyperspectral Image Ta...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Kun Shen Weixin Xie Haojin Tang Yanshan Li ATR National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Compared with grayscale and RGB images, hyperspectral image (HSI) can provide both spatial and spectral information of ground targets, which makes it possible to improve the efficiency and accuracy of target detection. Therefore, the research of HSI target detection algorithms has attracted widespread concern in recent years. With the development of hardware devices and the arrival of big data era, deep learning algorithms have been successfully applied to image processing, text recognition and other fields. However, due to the complex gathering environment of HSI, it is so difficult to obtain a large number of labeled samples, which limits the application of deep learning algorithms in HSI target detection. Therefore, a dense convolution Siamese network (DCSN) is proposed for HSI target detection, which improves the accuracy in the scenery of small-scale training samples. The main contributions of this paper include the following three points. First, we design a target sample generation method based on improved autoencoder to enhance target training data. Then, a background selection method based on density estimation is presented, which can acquire typical background samples effectively. Finally, a spectral feature extraction method based on dense convolution is proposed to extract the more discriminative spectral features. The experimental results of HSI target detection on Muufl Gulfport and San Diego datasets indicate that our proposed DCSN is able to achieve superior performance than the existing target detectors.

关键词： Deep learning Training Convolution Text recognition signal processing algorithms Training data Object detection

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GAUSSIAN PROCESSES OVER GRAPHS

GAUSSIAN PROCESSES OVER GRAPHS

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IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Venkitaraman, Arun Chatterjee, Saikat Handel, Peter KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden KTH Royal Inst Technol Div Informat Sci & Engn Stockholm Sweden

ISBN: (纸本)9781509066315

Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given the input when all the signals are deterministic. In this work, we consider the development of a stochastic or Bayesian variant of KRG. Using priors and likelihood functions, our goal is to systematically derive a predictive distribution for the smooth graph signal target given the training data and a new input. We show that this naturally results in a Gaussian process formulation which we call Gaussian Processes over Graphs (GPG). Experiments with real-world datasets show that the performance of GPG is superior to a conventional Gaussian Process (without the graph-structure) for small training data sizes and under noisy training.

关键词： Graph signal processing Gaussian processes Bayesian estimation kernel regression graph-Laplacian

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Using data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda 14

Using Data Analytics to Strengthen Monitoring and Surveillan...

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14th International Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and signal processing (BIOsignalS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Nantongo, Bartha Alexandra Nabukenya, Josephine Nabende, Peter Makerere Univ Coll Comp & Informat Syst Dept Informat Syst POB 7062 Kampala Uganda

ISBN: (纸本)9789897584909

Immunization coverage is a traditional key performance indicator that enables stakeholders to monitor child health, investigate gaps, and take remedial actions. It is continuously challenged by validity due to the neglect of unstructured data and process indicators that track small changes/milestones. While empirical evidence indicates digitalized immunization systems establish coverage from structured data, renowned administrative and household survey estimates are often inaccurate/untimely. Government instituted awareness, accessibility, and results-based performance approaches, but stakeholders are challenged by accurate monitoring of performance against Global Vaccination Action Plan coverage targets. This heightens inappropriate strategy implementation leading to persistent low coverage and declining trends. There is scanty literature substantiating the essence of comprehensive immunization indicators in monitoring evidence-based and timely interventions. For this reason, health workers failed to appreciate immunization process indicators and monitoring role. The study aims at developing a real-time immunization coverage monitoring framework that supports evidence-based strategy implementation using prescriptive analytics. The envisaged artifact analyzes a variety of data and monitors immunization performance against comprehensive indicators. It is a less resource-demanding strategy that prompts accurate and real-time insights to support intervention implementation decisions. This study will follow an explanatory research approach by first collecting quantitative data and later qualitative for in-depth analysis.

关键词： data Analytics Monitoring Routine Immunization Coverage Performance

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Research on recognition algorithm of LSS based on video in airport clearance area 2

Research on recognition algorithm of LSS based on video in a...

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2nd IEEE International conference on Big data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021

作者： Qu, Jingyi Bi, Xinjie Liu, Shanliang Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin300300 China

ISBN: (纸本)9780738131221

LSS (Low, small and Slow) targets have characteristics of low cost, simple operation, easy to carry, low take-off requirements, strong sudden takeoff, and difficult to find and handle. With the development of aviation industry, the cases of LSS flying illegally and used in terrorist attacks are increasing day by day. It will not only cause great negative effects on economy, but also have serious influence on the national security and the normal development of the national economy. In order to eliminate the threat of illegal flight of UAV in the airport clearance area to the safe operation of airport, a LSS recognition algorithm based on the deep learning was proposed in the paper. Compared with other detection types, the video detection was used in the algorithm, which has many advantages such as strong visualization, low cost and fast detection speed. By using the Yolov3 object detection model, one of the most popular deep learning methods, the problem of low accuracy of small objects such as LSS can be solved. The experiments show that the Yolov3 model can improve the accuracy for small objects and can effectively detect the LSS. Thus, the necessary precondition for the subsequent LSS countermeasures can be provided, and the safe operation of the airport can be ensured. © 2021 IEEE.

关键词： Object detection

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Practical Detection using Sparse signal Separation

Practical Detection using Sparse Signal Separation

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IEEE International Radar conference (RADAR)

作者： Rosenberg, Luke Duk, Vichet Ng, Brian Def Sci & Technol Grp Canberra ACT Australia Univ Adelaide Adelaide SA Australia Fraunhofer FHR Wachtberg Germany

ISBN: (纸本)9781728168135

Due to the dynamic motion of the sea, radar detection of small maritime targets can be difficult. Airborne maritime surveillance platforms are also increasingly required to operate at higher altitudes where backscatter from the sea becomes stronger. The focus of this paper is to investigate three sparse signal separation formulations using the short time Fourier transform as a dictionary. This approach has been demonstrated as effective in separating both stationary and moving targets from sea clutter, but relies on the tuning of different parameters. The first part of this work looks at the selection of the penalty parameter, which is essential to achieve good separation. Then a number of practical detection schemes are presented that allow control of the false alarm rate. The algorithm performance is demonstrated using Monte-Carlo simulation with synthetic targets injected into the Ingara medium grazing angle sea-clutter data set.

关键词： Sea clutter radar detection compressive sensing

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small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration

Small Target Detection Method in Sea Clutter Based on Interf...

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IEEE International conference on signal and Image processing (ICSIP)

作者： Shuwen Xu Yitu Ma Xiaohui Bai National Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (纸本)9781665446006

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. The radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". The multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. The measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods.

关键词： Radar cross-sections Radar clutter Surveillance Radar detection Radar Object detection Feature extraction

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A Deep Learning-based Super-resolution Model for Bistatic SAR Image

A Deep Learning-based Super-resolution Model for Bistatic SA...

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2021 International conference on Electronics, Circuits and Information Engineering, ECIE 2021

作者： Cen, Xi Song, Xuan Li, Yachao Wu, Chunfeng Xidian University School of Life Science and Technology Xi'An China Xidian University Key Laboratory for Radar Signal Processing Xi'An China China Sanjiang Space Group Wu Han China

ISBN: (纸本)9781665418690

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images. © 2021 IEEE.

关键词： Synthetic aperture radar

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Intelligent System for Real time detection and classification of Aerial targets using CNN

Intelligent System for Real time detection and classificatio...

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International conference on ent Computing and Control Systems (ICICCS)

作者： Madhusudan Upadhyay Somanchi Krishna Murthy A A Bazil Raj Defence Institute of Advanced Technology Pune India

The advancements in drone technology has led to increased usage of drones in civil and military which poses significant security related challenges. The small size of drones and birds pose a significant problem for their accurate detection in real time. These aerial targets have reduced RCS and operate at low altitudes with speeds of 40-80kmph making it difficult to accurately detect and classify them. This paper focusses on the design and development of an intelligent system for the detection and classification of these aerial targets by obtaining their micro-Doppler characteristics. The work integrates the signal processing techniques of radar-based detection with neural network classifier model for real time target classification.

关键词： Training Neural networks data acquisition Process control Object detection signal processing Real-time systems

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