scholarly journals Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiangsuo Fan ◽  
Zhiyong Xu ◽  
Jianlin Zhang ◽  
Yongmei Huang ◽  
Zhenming Peng
Keyword(s):  
Detection Method ◽  
Detection Algorithm ◽  
Local Energy ◽  
Signal Noise ◽  
Sequential Image ◽  
Energy Center ◽  
Overall Performance ◽  
Noise Ratio ◽  
Motion Characteristics ◽  
Small Targets

In order to detect infrared (IR) dim and small targets in a strong clutter background, a method based on local energy center of sequential image is proposed. This paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HOC). Finally, on the basis of image preprocessing, the paper constructs a sequential image energy center detection algorithm that integrates the neighborhood, continuity, area, and energy and other motion characteristics of the target. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HOC significantly increased the signal-noise ratio of images; when the signal-noise ratio (SNR) is lower than 2.5 dB, the proposed method could effectively eliminate noise and detect targets.

scholarly journals Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Fan Xiangsuo ◽  
Hongwei Guo ◽  
Xu Zhiyong ◽  
Biao Li
Keyword(s):  
Detection Algorithm ◽  
Spatial Domain ◽  
Mean Value ◽  
Local Motion ◽  
Detection Rates ◽  
Motion Characteristics ◽  
Small Targets ◽  
The Time Domain ◽  
Diffusion Function ◽  
Sequential Images

In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.

scholarly journals Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Fan Xiangsuo ◽  
Xu Zhiyong
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Background Modeling ◽  
Small Target ◽  
Local Energy ◽  
Dynamic Scenes ◽  
Detection Ability ◽  
Small Target Detection ◽  
The Difference ◽  
Small Targets

In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

scholarly journals Traffic Sign Detection Method Based on Improved SSD

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 475
Author(s):  
Shuai You ◽  
Qiang Bi ◽  
Yimu Ji ◽  
Shangdong Liu ◽  
Yujian Feng ◽  
...  
Keyword(s):  
Feature Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Adverse Weather ◽  
Convolution Kernels ◽  
Small Targets ◽  
Detection Effect

Due to changes in illumination, adverse weather conditions, and interference from signs similar to real traffic signs, the false detection of traffic signs is possible. Nevertheless, in order to improve the detection effect of small targets, baseline SSD (single shot multibox detector) adopts a multi-scale feature detection method to improve the detection effect to some extent. The detection effect of small targets is improved, but the number of calculations needed for the baseline SSD network is large. To this end, we propose a lightweight SSD network algorithm. This method uses some 1 × 1 convolution kernels to replace some of the 3 × 3 convolution kernels in the baseline network and deletes some convolutional layers to reduce the calculation load of the baseline SSD network. Then the color detection algorithm based on the phase difference method and the connected component calculation are used to further filter the detection results, and finally, the data enhancement strategy based on the image appearance transformation is used to improve the balance of the dataset. The experimental results show that the proposed method is 3% more accurate than the baseline SSD network, and more importantly, the detection speed is also increased by 1.2 times.

Signal/Noise Ratio and Elemental Detectivity by Eels

1982 ◽  
Vol 40 ◽  
pp. 488-489
Author(s):  
R. F. Egerton
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Elemental Analysis ◽  
Electron Energy Loss Spectroscopy ◽  
Emission Spectroscopy ◽  
X Ray ◽  
Signal Noise ◽  
Characteristic Signal ◽  
Noise Ratio ◽  
Electron Energy Loss

An important parameter governing the sensitivity and accuracy of elemental analysis by electron energy-loss spectroscopy (EELS) or by X-ray emission spectroscopy is the signal/noise ratio of the characteristic signal.

ASSESSMENT OF THE SIGNAL/NOISE RATIO IN A BINARY SIGNAL

2012 ◽  
Vol 71 (5) ◽  
pp. 445-453
Author(s):  
M. D. Rasnikov ◽  
I. T. Rozhkov
Keyword(s):  
Signal Noise ◽  
Binary Signal ◽  
Noise Ratio

Case Study of Electro-Optical Probing Signal with High Signal-Noise Ratio

2019 ◽  
Author(s):  
Ryan Xiao ◽  
William Wang ◽  
Ang Li ◽  
Shengqiu Xu ◽  
Binghai Liu
Keyword(s):  
Failure Mode ◽  
High Signal ◽  
Signal Noise ◽  
Electrical Failure ◽  
Current Path ◽  
Probing Signal ◽  
Optical Probing ◽  
Noise Ratio ◽  
Case Collection

Abstract With the development of semiconductor technology and the increment quantity of metal layers in past few years, backside EFA (Electrical Failure Analysis) technology has become the dominant method. In this paper, abnormally high Signal Noise Ratio (SNR) signal captured by Electro-Optical Probing (EOP)/Laser Voltage Probing (LVP) from backside is shown and the cause of these phenomena are studied. Based on the real case collection, two kinds of failure mode are summarized, and simulated experiments are performed. The results indicate that when a current path from power to ground is formed, the high SNR signal can be captured at the transistor which was on this current path. It is helpful of this consequence for FA to identify the failure mode by high SNR signal.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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