scholarly journals On the testing and validation of stray light attenuation for microsatellite star tracker baffles.

2021 ◽  
Author(s):  
Martin Marciniak
Keyword(s):  
Ray Tracing ◽  
Light Attenuation ◽  
Analytical Techniques ◽  
Stray Light ◽  
Surface Scattering ◽  
Star Tracker ◽  
Preliminary Design ◽  
False Detection Rate ◽  
Engineering Model ◽  
False Detection

This thesis describes strategies to perform stray light testing in an earthbound laboratory while accounting for atmospheric and surface scattering phenomena that make these measurements difficult. We present a method to analyze and predict the stray light performance for a baffled star tracker optical system. This method involves a hybrid stray light analysis procedure that combines experimental measurements of a star tracker lens optics and uses ray-tracing to obtain attenuation curves. We demonstrate these analytical techniques using an engineering model ST-16 star tracker from Sinclair Interplanetary along with a baffle prototype. The system attenuation curve's accuracy is validated by comparing independently measured baffle attenuation curves with equivalent ray-tracing models. Additionally, exclusion angles are defined for the ST-16 sensor by calculating the false detection rate that varies with system attenuation levels. These techniques provide a versatile alternative to conventional testing for preliminary design stages for a star tracker baffle that emphasizes the use of modest infrastructure.

scholarly journals On the testing and validation of stray light attenuation for microsatellite star tracker baffles.

2021 ◽  
Author(s):  
Martin Marciniak
Keyword(s):  
Ray Tracing ◽  
Light Attenuation ◽  
Analytical Techniques ◽  
Stray Light ◽  
Surface Scattering ◽  
Star Tracker ◽  
Preliminary Design ◽  
False Detection Rate ◽  
Engineering Model ◽  
False Detection

This thesis describes strategies to perform stray light testing in an earthbound laboratory while accounting for atmospheric and surface scattering phenomena that make these measurements difficult. We present a method to analyze and predict the stray light performance for a baffled star tracker optical system. This method involves a hybrid stray light analysis procedure that combines experimental measurements of a star tracker lens optics and uses ray-tracing to obtain attenuation curves. We demonstrate these analytical techniques using an engineering model ST-16 star tracker from Sinclair Interplanetary along with a baffle prototype. The system attenuation curve's accuracy is validated by comparing independently measured baffle attenuation curves with equivalent ray-tracing models. Additionally, exclusion angles are defined for the ST-16 sensor by calculating the false detection rate that varies with system attenuation levels. These techniques provide a versatile alternative to conventional testing for preliminary design stages for a star tracker baffle that emphasizes the use of modest infrastructure.

Micro star tracker with a curved vane for a short baffle length and sharp stray light attenuation

2020 ◽  
Vol 59 (13) ◽  
pp. 4131
Author(s):  
Sukhan Lee ◽  
Rashid Saleem ◽  
Sang-Seok Lee
Keyword(s):  
Light Attenuation ◽  
Stray Light ◽  
Star Tracker

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

Stray-light reduction in a WFOV star tracker lens

1991 ◽  
Author(s):  
Isabella T. Lewis ◽  
Arno G. Ledebuhr ◽  
Timothy S. Axelrod ◽  
Scott A. Ruddell
Keyword(s):  
Stray Light ◽  
Star Tracker ◽  
Light Reduction

scholarly journals A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Li ◽  
Yao Liu ◽  
Zhengfan Zhao ◽  
Yue Zhang ◽  
Li He
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Training Model ◽  
Learning Approach ◽  
Flow State ◽  
False Detection Rate ◽  
False Detection ◽  
Road Vehicles ◽  
Traffic Video ◽  
Flow States

Vehicle detection is expected to be robust and efficient in various scenes. We propose a multivehicle detection method, which consists of YOLO under the Darknet framework. We also improve the YOLO-voc structure according to the change of the target scene and traffic flow. The classification training model is obtained based on ImageNet and the parameters are fine-tuned according to the training results and the vehicle characteristics. Finally, we obtain an effective YOLO-vocRV network for road vehicles detection. In order to verify the performance of our method, the experiment is carried out on different vehicle flow states and compared with the classical YOLO-voc, YOLO 9000, and YOLO v3. The experimental results show that our method achieves the detection rate of 98.6% in free flow state, 97.8% in synchronous flow state, and 96.3% in blocking flow state, respectively. In addition, our proposed method has less false detection rate than previous works and shows good robustness.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

2021 ◽  
Author(s):  
Yuqing Zhao ◽  
Jinlu Jia ◽  
Di Liu ◽  
Yurong Qian
Keyword(s):  
Target Detection ◽  
Loss Function ◽  
Detection Rate ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Small Scale ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Bounding Box

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

A Approach to Change Detection for HR Image

2014 ◽  
Vol 971-973 ◽  
pp. 1449-1453
Author(s):  
Zuo Wei Huang ◽  
Shu Guang Wu ◽  
Tao Xin Zhang
Keyword(s):  
Change Detection ◽  
Detection Rate ◽  
False Detection Rate ◽  
False Detection ◽  
High Detection Rate ◽  
High Detection ◽  
Self Adaptation ◽  
Ica Algorithm ◽  
Different Characteristics ◽  
Better Than

Hyperspectral remote sensing is the multi-dimensional information obtaining technology,which combines target detection and spectral imaging technology together, In order to accord with the condition of hyperspectral imagery，the paper developed an optimized ICA algorithm for change detection to describe the statistical distribution of the data. By processing these abundance maps, change of different classes of objects can be obtained..A approach is capable of self-adaptation, and can be applied to hyperspectral images with different characteristics. Experiment results demonstrate that the ICA-based hyperspectral change detection performs better than other traditional methods with a high detection rate and a low false detection rate.

scholarly journals ORIGIN: Blind detection of faint emission line galaxies in MUSE datacubes

2020 ◽  
Vol 635 ◽  
pp. A194 ◽  
Author(s):  
David Mary ◽  
Roland Bacon ◽  
Simon Conseil ◽  
Laure Piqueras ◽  
Antony Schutz
Keyword(s):  
Emission Line ◽  
Detection Rate ◽  
Data Cube ◽  
Ratio Test ◽  
Blind Detection ◽  
False Detection Rate ◽  
False Detection ◽  
Local Maxima ◽  
Lyman Alpha Emitters ◽  
Integral Field Spectrograph

Context. One of the major science cases of the Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph is the detection of Lyman-alpha emitters at high redshifts. The on-going and planned deep fields observations will allow for one large sample of these sources. An efficient tool to perform blind detection of faint emitters in MUSE datacubes is a prerequisite of such an endeavor. Aims. Several line detection algorithms exist but their performance during the deepest MUSE exposures is hard to quantify, in particular with respect to their actual false detection rate, or purity. The aim of this work is to design and validate an algorithm that efficiently detects faint spatial-spectral emission signatures, while allowing for a stable false detection rate over the data cube and providing in the same time an automated and reliable estimation of the purity. Methods. The algorithm implements (i) a nuisance removal part based on a continuum subtraction combining a discrete cosine transform and an iterative principal component analysis, (ii) a detection part based on the local maxima of generalized likelihood ratio test statistics obtained for a set of spatial-spectral profiles of emission line emitters and (iii) a purity estimation part, where the proportion of true emission lines is estimated from the data itself: the distribution of the local maxima in the “noise only” configuration is estimated from that of the local minima. Results. Results on simulated data cubes providing ground truth show that the method reaches its aims in terms of purity and completeness. When applied to the deep 30 h exposure MUSE datacube in the Hubble Ultra Deep Field, the algorithms allows for the confirmed detection of 133 intermediate redshifts galaxies and 248 Lyα emitters, including 86 sources with no Hubble Space Telescope counterpart. Conclusions. The algorithm fulfills its aims in terms of detection power and reliability. It is consequently implemented as a Python package whose code and documentation are available on GitHub and readthedocs.

Ray tracing based simulation of stray light effect for geostationary ocean color imager

2013 ◽  
Author(s):  
Eunsong Oh ◽  
Jinsuk Hong ◽  
Sug-Whan Kim ◽  
Seongick Cho ◽  
Joo-Hyung Ryu
Keyword(s):  
Ray Tracing ◽  
Ocean Color ◽  
Stray Light ◽  
Light Effect
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