scholarly journals Heterogeneity Detection Method for Transmission Multispectral Imaging Based on Contour and Spectral Features

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5369
Author(s):  
Yanjun Wang ◽  
Gang Li ◽  
Wenjuan Yan ◽  
Guoquan He ◽  
Ling Lin
Keyword(s):  
Multispectral Imaging ◽  
Shape Function ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Detection Accuracy ◽  
Spectral Features ◽  
Signal Modulation ◽  
Early Screening ◽  
Modulation And Demodulation ◽  
Strong Scattering

Transmission multispectral imaging (TMI) has potential value for medical applications, such as early screening for breast cancer. However, because biological tissue has strong scattering and absorption characteristics, the heterogeneity detection capability of TMI is poor. Many techniques, such as frame accumulation and shape function signal modulation/demodulation techniques, can improve detection accuracy. In this work, we develop a heterogeneity detection method by combining the contour features and spectral features of TMI. Firstly, the acquisition experiment of the phantom multispectral images was designed. Secondly, the signal-to-noise ratio (SNR) and grayscale level were improved by combining frame accumulation with shape function signal modulation and demodulation techniques. Then, an image exponential downsampling pyramid and Laplace operator were used to roughly extract and fuse the contours of all heterogeneities in images produced by a variety of wavelengths. Finally, we used the hypothesis of invariant parameters to do heterogeneity classification. Experimental results show that these invariant parameters can effectively distinguish heterogeneities with various thicknesses. Moreover, this method may provide a reference for heterogeneity detection in TMI.

scholarly journals An Improved Jitter Detection Method Based on Parallax Observation of Multispectral Sensors for Gaofen-1 02/03/04 Satellites

2018 ◽  
Vol 11 (1) ◽  
pp. 16 ◽  
Author(s):  
Ying Zhu ◽  
Mi Wang ◽  
Yufeng Cheng ◽  
Luxiao He ◽  
Lin Xue
Keyword(s):  
High Resolution ◽  
Multispectral Imaging ◽  
Detection Method ◽  
Negative Influence ◽  
Detection Accuracy ◽  
Comparison Results ◽  
Geometric Quality ◽  
Along Track ◽  
Internal Error ◽  
Multispectral Sensors

Gaofen-1 02/03/04 satellites, the first civilian high resolution optical operational constellation in China, have Earth observation capabilities with panchromatic/multispectral imaging at 2/8 m resolution. Satellite jitter, the fluctuation of satellite points, has a negative influence on the geometric quality of high-resolution optical satellite imagery. This paper presents an improved jitter detection method based on parallax observation of multispectral sensors for Gaofen-1 02/03/04 satellites, which can eliminate the effect of the relative internal error induced by lens distortion, and accurately estimate the parameters of satellite jitter. The relative internal error is estimated by polynomial modelling and removed from the original parallax image generated by pixel-to-pixel image matching between two bands of images. The accurate relative time-varying error and absolute distortion caused by satellite jitter could be estimated by using the sine function. Three datasets of multispectral images captured by Gaofen-1 02/03/04 satellites were used to conduct the experiments. The results show that the relative system errors in both the across- and along-track directions can be modelled with a quadratic polynomial, and satellite jitter with a frequency of 1.1–1.2 Hz in the across-track direction was detected for the first time. The amplitude of the jitter differed in the three datasets. The largest amplitude, from satellite 04, is 1.3 pixels. The smallest amplitude, from satellite 02, is 0.077 pixels. The reliability and accuracy of the detection results were verified by using two groups of band combinations and ortho-images with a 1 m resolution. The comparison results show that the detection accuracy is improved by approximately 30% using the proposed method.

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals Locating and Imaging through Scattering Medium in a Large Depth

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 90
Author(s):  
Shuo Zhu ◽  
Enlai Guo ◽  
Qianying Cui ◽  
Lianfa Bai ◽  
Jing Han ◽  
...  
Keyword(s):  
Phase Space ◽  
Signal To Noise Ratio ◽  
Speckle Pattern ◽  
Heuristic Method ◽  
Scattering Medium ◽  
Scattering Media ◽  
Signal To Noise ◽  
Practical Applications ◽  
Large Depth ◽  
Strong Scattering

Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

scholarly journals Cellulose and Vanadium Plasmonic Sensor to Measure Ni2+ Ions

2021 ◽  
Vol 11 (7) ◽  
pp. 2963
Author(s):  
Nur Alia Sheh Omar ◽  
Yap Wing Fen ◽  
Irmawati Ramli ◽  
Umi Zulaikha Mohd Azmi ◽  
Hazwani Suhaila Hashim ◽  
...  
Keyword(s):  
Thin Film ◽  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Signal To Noise Ratio ◽  
Affinity Constant ◽  
Detection Accuracy ◽  
Plasmonic Sensor ◽  
Spr Sensor ◽  
Resonance Sensor

A novel vanadium–cellulose composite thin film-based on angular interrogation surface plasmon resonance (SPR) sensor for ppb-level detection of Ni(II) ion was developed. Experimental results show that the sensor has a linear response to the Ni(II) ion concentrations in the range of 2–50 ppb with a determination coefficient (R2) of 0.9910. This SPR sensor can attain a maximum sensitivity (0.068° ppb−1), binding affinity constant (1.819 × 106 M−1), detection accuracy (0.3034 degree−1), and signal-to-noise-ratio (0.0276) for Ni(II) ion detection. The optical properties of thin-film targeting Ni(II) ions in different concentrations were obtained by fitting the SPR reflectance curves using the WinSpall program. All in all, the proposed Au/MPA/V–CNCs–CTA thin-film-based surface plasmon resonance sensor exhibits better sensing performance than the previous film-based sensor and demonstrates a wide and promising technology candidate for environmental monitoring applications in the future.

A real time detection method for abnormal strapping of steel coil based on CCD active imaging

2021 ◽  
Author(s):  
Chuan Ye ◽  
Liming Zhao ◽  
Qiyan Wang ◽  
Bo Pan ◽  
Youchun Xie ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Detection Method ◽  
Saliency Detection ◽  
Automatic Segmentation ◽  
Engineering Application ◽  
Detection Accuracy ◽  
Steel Coil ◽  
Detection Model ◽  
Laser Stripe ◽  
Active Imaging

Abstract In order to accurately detect the abnormal looseness of strapping in the process of steel coil hoisting, an accurate detection method of strapping abnormality based on CCD structured light active imaging is proposed. Firstly, a maximum entropy laser stripe automatic segmentation model integrating multi-scale saliency features is constructed. With the help of saliency detection model, the purpose is to reduce the interference of the environment to the laser stripe and highlight the distinguishability between the stripe and the background. Then, the maximum entropy is used to segment the fused saliency features and accurately extract the stripe contour. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, the stripe center line is extracted based on the stripe distribution normal direction, and the abnormal strapping is recognized online according to the stripe center. Experiments show that the proposed method is effective in terms of detection accuracy and time efficiency, and has certain engineering application value.

The Study on High Precision 3D Detection Method of Steel Plate Surface Based on Structured Light

2012 ◽  
Vol 572 ◽  
pp. 338-342 ◽  
Author(s):  
Zhi Guo Liang ◽  
Quan Yang ◽  
Ke Xu ◽  
Fei He ◽  
Xiao Chen Wang ◽  
...  
Keyword(s):  
Surface Quality ◽  
Steel Plate ◽  
Detection Method ◽  
Structured Light ◽  
Detection Methods ◽  
Measurement Technology ◽  
Detection Accuracy ◽  
Plate Surface ◽  
Factors Affecting

Structured light 3D measurement technology with its simple structure, non-contact measurement, fast measurement speed and other advantages, has been widely used. Steel plate surface quality detection is not confined to the two-dimensional feature of gray detection, and local topography measurement for surface quality of steel plate detection becomes increasingly important. In this paper, steel plate surface 3D detection method based on structured light and the factors affecting the measurement accuracy are analyzed. Several effective methods of improving 3D detection accuracy are put forward. Compared with the traditional structured light 3D detection methods, the detection accuracy of new methods is remarkably improved, thus possessing better application values.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals Permission Sensitivity-Based Malicious Application Detection for Android

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Yijin Geng ◽  
Junbo Wang ◽  
Shang Gao ◽  
Wei Shi
Keyword(s):  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Private Data ◽  
Weight Allocation

Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

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