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10 pages, 2117 KiB

Open AccessArticle

A CMOS Compatible Pyroelectric Mid-Infrared Detector Based on Aluminium Nitride

by Christian Ranacher, Cristina Consani, Andreas Tortschanoff, Lukas Rauter, Dominik Holzmann, Clement Fleury, Gerald Stocker, Andrea Fant, Herbert Schaunig, Peter Irsigler, Thomas Grille and Bernhard Jakoby

Sensors 2019, 19(11), 2513; https://doi.org/10.3390/s19112513 - 31 May 2019

Cited by 22 | Viewed by 6368

Abstract

The detection of infrared radiation is of great interest for a wide range of applications, such as absorption sensing in the infrared spectral range. In this work, we present a CMOS compatible pyroelectric detector which was devised as a mid-infrared detector, comprising aluminium [...] Read more.

The detection of infrared radiation is of great interest for a wide range of applications, such as absorption sensing in the infrared spectral range. In this work, we present a CMOS compatible pyroelectric detector which was devised as a mid-infrared detector, comprising aluminium nitride (AlN) as the pyroelectric material and fabricated using semiconductor mass fabrication processes. To ensure thermal decoupling of the detector, the detectors are realized on a Si₃N₄/SiO₂ membrane. The detectors have been tested at a wavelength close to the CO₂ absorption region in the mid-infrared. Devices with various detector and membrane sizes were fabricated and the influence of these dimensions on the performance was investigated. The noise equivalent power of the first demonstrator devices connected to a readout circuit was measured to be as low as

5.3 \times 10^{- 9} W / \sqrt{Hz}

. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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19 pages, 13587 KiB

Open AccessArticle

IR-Band Conversion of Target and Background Using Surface Temperature Estimation and Error Compensation for Military IR Sensor Simulation

by Taewuk Bae, Youngchoon Kim and Sangho Ahn

Sensors 2019, 19(11), 2455; https://doi.org/10.3390/s19112455 - 29 May 2019

Cited by 10 | Viewed by 5781

Abstract

Military infrared (IR) imaging systems utilize one or more IR wavelength-bands, among short wavelength IR (SWIR), middle wavelength IR (MWIR), and long wavelength IR (LWIR) band. The IR image wavelength-band conversion which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image [...] Read more.

Military infrared (IR) imaging systems utilize one or more IR wavelength-bands, among short wavelength IR (SWIR), middle wavelength IR (MWIR), and long wavelength IR (LWIR) band. The IR image wavelength-band conversion which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image is needed for IR signature modeling and image synthesis in the IR systems. However, the IR wavelength-band conversion is very challenging because absorptivity and transmittance of objects and background (atmosphere) are different according to the IR wavelength band and because radiation and reflectance characteristics of the SWIR are very different from the LWIR and MWIR. Therefore, the IR wavelength-band conversion in this paper applies to only IR targets and monotonous backgrounds at a long distance for military purposes. This paper proposes an IR wavelength-band conversion method which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image by using the surface temperature estimation of an object and the error attenuation method for the estimated temperature. The surface temperature of the object is estimated by an approximated Planck’s radiation equation and the error of estimated temperature is corrected by using the slope information of exact radiance along with the approximated one. The corrected surface temperature is used for generating another IR wavelength-band image. The verification of the proposed method is demonstrated through the simulations using actual IR images obtained by thermal equipment. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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29 pages, 10180 KiB

Open AccessArticle

Thermal Analysis of a Magnetic Brake Using Infrared Techniques and 3D Cell Method with a New Convective Constitutive Matrix

by José Miguel Monzón-Verona, Pablo Ignacio González-Domínguez, Santiago García-Alonso, Francisco Jorge Santana-Martín and Juan Francisco Cárdenes-Martín

Sensors 2019, 19(9), 2028; https://doi.org/10.3390/s19092028 - 30 Apr 2019

Cited by 3 | Viewed by 4700

Abstract

In this work we analyse the temperature distribution in a conductor disk in transitory regime. The disk is in motion in a stationary magnetic field generated by a permanent magnet and so, the electric currents induced inside it generate heat. The system acts [...] Read more.

In this work we analyse the temperature distribution in a conductor disk in transitory regime. The disk is in motion in a stationary magnetic field generated by a permanent magnet and so, the electric currents induced inside it generate heat. The system acts as a magnetic brake and is analysed using infrared sensor techniques. In addition, for the simulation and analysis of the magnetic brake, a new thermal convective matrix for the 3D Cell Method (CM) is proposed. The results of the simulation have been verified by comparing the numerical results with those obtained by the Finite Element Method (FEM) and with experimental data obtained by infrared technology. The difference between the experimental results obtained by infrared sensors and those obtained in the simulations is less than 0.0459%. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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25 pages, 3517 KiB

Open AccessArticle

Anti-Interference Aircraft-Tracking Method in Infrared Imagery

by Sijie Wu, Kai Zhang, Saisai Niu and Jie Yan

Sensors 2019, 19(6), 1289; https://doi.org/10.3390/s19061289 - 14 Mar 2019

Cited by 12 | Viewed by 4329

Abstract

In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting [...] Read more.

In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting from a diversity of attacking angles. To address this challenge, an aircraft-tracking algorithm was proposed, which provides robustness in tracking the aircraft against the decoys. We reveal the inherent structure and infrared signature of the aircraft, which are used as discriminative features to track the aircraft. The anti-interference method was developed based on simulated images but validate the effectiveness on both real infrared image sequences without decoys and simulated infrared imagery. For frequent occlusion caused by the decoys, the mechanism of occlusion detection is exploited according to the variation of the model distance in tracking process. To have a comprehensive evaluation of tracking performance, infrared-image sequences with different attack angles were simulated, and experiments on benchmark trackers were performed to quantitatively evaluate tracking performance. The experiment results demonstrate that our aircraft-tracking method performs favorably against state-of-the-art trackers. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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17 pages, 6201 KiB

Open AccessArticle

Sensitivity Improvement of Extremely Low Light Scenes with RGB-NIR Multispectral Filter Array Sensor

by Seunghoon Jee and Moon Gi Kang

Sensors 2019, 19(5), 1256; https://doi.org/10.3390/s19051256 - 12 Mar 2019

Cited by 11 | Viewed by 6646

Abstract

Recently, several red-green-blue near-infrared (RGB-NIR) multispectral filter arrays (MFAs), which include near infrared (NIR) pixels, have been proposed. For extremely low light scenes, the RGB-NIR MFA sensor has been extended to receive NIR light, by adding NIR pixels to supplement for the insufficient [...] Read more.

Recently, several red-green-blue near-infrared (RGB-NIR) multispectral filter arrays (MFAs), which include near infrared (NIR) pixels, have been proposed. For extremely low light scenes, the RGB-NIR MFA sensor has been extended to receive NIR light, by adding NIR pixels to supplement for the insufficient visible band light energy. However, the resolution reconstruction of the RGB-NIR MFA, using demosaicing and color restoration methods, is based on the correlation between the NIR pixels and the pixels of other colors; this does not improve the RGB channel sensitivity with respect to the NIR channel sensitivity. In this paper, we propose a color restored image post-processing method to improve the sensitivity and resolution of an RGB-NIR MFA. Although several linear regression based color channel reconstruction methods have taken advantage of the high sensitivity NIR channel, it is difficult to accurately estimate the linear coefficients because of the high level of noise in the color channels under extremely low light conditions. The proposed method solves this problem in three steps: guided filtering, based on the linear similarity between the NIR and color channels, edge preserving smoothing to improve the accuracy of linear coefficient estimation, and residual compensation for lost spatial resolution information. The results show that the proposed method is effective, while maintaining the NIR pixel resolution characteristics, and improving the sensitivity in terms of the signal-to-noise ratio by approximately 13 dB. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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17 pages, 7800 KiB

Open AccessArticle

Automatic and Robust Infrared-Visible Image Sequence Registration via Spatio-Temporal Association

by Bingqing Zhao, Tingfa Xu, Yiwen Chen, Tianhao Li and Xueyuan Sun

Sensors 2019, 19(5), 997; https://doi.org/10.3390/s19050997 - 26 Feb 2019

Cited by 7 | Viewed by 3239

Abstract

To solve the problems of the large differences in gray value and inaccurate positioning of feature information during infrared-visible image registration, we propose an automatic and robust algorithm for registering planar infrared-visible image sequences through spatio-temporal association. In particular, we first create motion [...] Read more.

To solve the problems of the large differences in gray value and inaccurate positioning of feature information during infrared-visible image registration, we propose an automatic and robust algorithm for registering planar infrared-visible image sequences through spatio-temporal association. In particular, we first create motion vector distribution descriptors which represent the temporal motion information of foreground contours in adjacent frames to complete coarse registration without feature extraction. Then, for precise registration, we extracted FAST corners of the foreground, which are described by the spatial location distribution of contour points based on connected blob detection, and match these corners using bidirectional optimal maximum strategy. Finally, a reservoir updated by Better-In, Worse-Out (BIWO) strategy is established to save matched point pairs and obtain the optimal global transformation matrix. Extensive evaluations on the LITIV dataset well demonstrate the effectiveness of the proposed algorithm. Particularly, our algorithm achieves lower registration overlapping errors than the other two state-of-the-arts. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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19 pages, 6193 KiB

Open AccessArticle

Single Infrared Image-Based Stripe Nonuniformity Correction via a Two-Stage Filtering Method

by Qingjie Zeng, Hanlin Qin, Xiang Yan, Shuowen Yang and Tingwu Yang

Sensors 2018, 18(12), 4299; https://doi.org/10.3390/s18124299 - 6 Dec 2018

Cited by 16 | Viewed by 4809

Abstract

The presence of stripe nonuniformity severely degrades the image quality and affects the performance in many infrared (IR) sensing applications. Prior works correct the nonuniformity by using similar spatial representations, which inevitably damage some detailed structures of the image. In this paper, we [...] Read more.

The presence of stripe nonuniformity severely degrades the image quality and affects the performance in many infrared (IR) sensing applications. Prior works correct the nonuniformity by using similar spatial representations, which inevitably damage some detailed structures of the image. In this paper, we instead take advantage of spectral prior of stripe noise to solve its correction problem in single IR image. We first analyse the significant spectral difference between stripes and image structures and utilize this knowledge to characterize stripe nonuniformity. Then a two-stage filtering strategy is adopted combining spectral and spatial filtering. The proposed method enables stripe nonuniformity to be eliminated from coarse to fine, thus preserving image details well. Extensive experiments on simulated images and raw IR images demonstrate that the proposed method achieves superior correction performance over the recent state-of-the-art methods. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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11 pages, 2375 KiB

Open AccessArticle

High Precision Position Measurement Method for Laguerre-Gaussian Beams Using a Quadrant Detector

by Qian Li, Jiabin Wu, Yunshan Chen, Jingyuan Wang, Shijie Gao and Zhiyong Wu

Sensors 2018, 18(11), 4007; https://doi.org/10.3390/s18114007 - 16 Nov 2018

Cited by 11 | Viewed by 3667

Abstract

In this paper, we propose a new method to improve the position measurement accuracy for Laguerre-Gaussian beams on a quadrant detector (QD). First, the error effects of the detector diameter and the gap size are taken into account, and the position error compensation [...] Read more.

In this paper, we propose a new method to improve the position measurement accuracy for Laguerre-Gaussian beams on a quadrant detector (QD). First, the error effects of the detector diameter and the gap size are taken into account, and the position error compensation factor is introduced into the conventional formula. Then, in order to reduce the number of parameters, the concept of effective radius is proposed. Thus, a new analytical expression is obtained with a best fit using the least square method. It is verified by simulation that this approach can reduce the maximum error by 97.4% when the beam radius is 0.95 mm; meanwhile, the root mean square errors under different radii are all less than 0.004 mm. The results of simulation show that the new method could effectively improve the accuracy of the QD measurement for different radii. Therefore, the new method would have a good prospect in the engineering practice of beam position measurements. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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15 pages, 3094 KiB

Open AccessArticle

Temperature Measurement Method for Blast Furnace Molten Iron Based on Infrared Thermography and Temperature Reduction Model

by Dong Pan, Zhaohui Jiang, Zhipeng Chen, Weihua Gui, Yongfang Xie and Chunhua Yang

Sensors 2018, 18(11), 3792; https://doi.org/10.3390/s18113792 - 6 Nov 2018

Cited by 21 | Viewed by 7891

Abstract

The temperature measurement of blast furnace (BF) molten iron is a mandatory requirement in the ironmaking process, and the molten iron temperature is significant in estimating the molten iron quality and control blast furnace condition. However, it is not easy to realize real-time [...] Read more.

The temperature measurement of blast furnace (BF) molten iron is a mandatory requirement in the ironmaking process, and the molten iron temperature is significant in estimating the molten iron quality and control blast furnace condition. However, it is not easy to realize real-time measurement of molten iron temperature because of the harsh environment in the blast furnace casthouse and the high-temperature characteristics of molten iron. To achieve continuous detection of the molten iron temperature of the blast furnace, this paper proposes a temperature measurement method based on infrared thermography and a temperature reduction model. Firstly, an infrared thermal imager is applied to capture the infrared thermal image of the molten iron flow after the skimmer. Then, based on the temperature distribution of the molten iron flow region, a temperature mapping model is established to measure the molten iron temperature after the skimmer. Finally, a temperature reduction model is developed to describe the relationship between the molten iron temperature at the taphole and skimmer, and the molten iron temperature at the taphole is calculated according to the temperature reduction model and the molten iron temperature after the skimmer. Industrial experiment results illustrate that the proposed method can achieve simultaneous measurement of molten iron temperature at the skimmer and taphole and provide reliable temperature data for regulating the blast furnace. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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10 pages, 4568 KiB

Open AccessArticle

Melamine Faced Panels Defect Classification beyond the Visible Spectrum

by Cristhian A. Aguilera, Cristhian Aguilera and Angel D. Sappa

Sensors 2018, 18(11), 3644; https://doi.org/10.3390/s18113644 - 27 Oct 2018

Cited by 4 | Viewed by 4227

Abstract

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and [...] Read more.

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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15 pages, 3952 KiB

Open AccessArticle

Miniature Uncooled and Unchopped Fiber Optic Infrared Thermometer for Application to Cutting Tool Temperature Measurement

by Andrew D. Heeley, Matthew J. Hobbs, Hatim Laalej and Jon R. Willmott

Sensors 2018, 18(10), 3188; https://doi.org/10.3390/s18103188 - 20 Sep 2018

Cited by 12 | Viewed by 5510

Abstract

A new infrared thermometer, sensitive to wavelengths between 3 μm and 3.5 μm, has been developed. It is based on an Indium Arsenide Antimony (InAsSb) photodiode, a transimpedance amplifier, and a sapphire fiber optic cable. The thermometer used an uncooled photodiode sensor and [...] Read more.

A new infrared thermometer, sensitive to wavelengths between 3 μm and 3.5 μm, has been developed. It is based on an Indium Arsenide Antimony (InAsSb) photodiode, a transimpedance amplifier, and a sapphire fiber optic cable. The thermometer used an uncooled photodiode sensor and received infrared radiation that did not undergo any form of optical chopping, thereby, minimizing the physical size of the device and affording its attachment to a milling machine tool holder. The thermometer is intended for applications requiring that the electronics are located remotely from high-temperature conditions incurred during machining but also affording the potential for use in other harsh conditions. Other example applications include: processes involving chemical reactions and abrasion or fluids that would otherwise present problems for invasive contact sensors to achieve reliable and accurate measurements. The prototype thermometer was capable of measuring temperatures between 200 °C and 1000 °C with sapphire fiber optic cable coupling to high temperature conditions. Future versions of the device will afford temperature measurements on a milling machine cutting tool and could substitute for the standard method of embedding thermocouple wires into the cutting tool inserts. Similarly, other objects within harsh conditions could be measured using these techniques and accelerate developments of the thermometer to suit particular applications. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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12 pages, 5051 KiB

Open AccessArticle

Tunable Fabry-Perot Interferometer Designed for Far-Infrared Wavelength by Utilizing Electromagnetic Force

by Dong Geon Jung, Jun Yeop Lee, Jae Keon Kim, Daewoong Jung and Seong Ho Kong

Sensors 2018, 18(8), 2572; https://doi.org/10.3390/s18082572 - 6 Aug 2018

Cited by 4 | Viewed by 4936

Abstract

A tunable Fabry-Perot interferometer (TFPI)-type wavelength filter designed for the long-wavelength infrared (LWIR) region is fabricated using micro electro mechanical systems (MEMS) technology and the novel polydimethylsiloxane (PDMS) micro patterning technique. The structure of the proposed infrared sensor consists of a Fabry-Perot interferometer [...] Read more.

A tunable Fabry-Perot interferometer (TFPI)-type wavelength filter designed for the long-wavelength infrared (LWIR) region is fabricated using micro electro mechanical systems (MEMS) technology and the novel polydimethylsiloxane (PDMS) micro patterning technique. The structure of the proposed infrared sensor consists of a Fabry-Perot interferometer (FPI)-based optical filter and infrared (IR) detector. An amorphous Si-based thermal IR detector is located under the FPI-based optical filter to detect the IR-rays filtered by the FPI. The filtered IR wavelength is selected according to the air etalon gap between reflectors, which is defined by the thickness of the patterned PDMS. The 8 μm-thick PDMS pattern is fabricated on a 3 nm-thick Al layer used as a reflector. The air etalon gap is changed using the electromagnetic force between the permanent magnet and solenoid. The measured PDMS gap height is about 2 μm, ranging from 8 μm to 6 μm, with driving current varying from 0 mA to 600 mA, resulting in a tunable wavelength range of 4 μm. The 3-dB bandwidth (full width at half maximum, FWHM) of the proposed filter is 1.5 nm, while the Free Spectral Range (FSR) is 8 μm. Experimental results show that the proposed TFPI can detect a specific wavelength at the long LWIR region. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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15 pages, 2787 KiB

Open AccessArticle

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

by Lei Feng, Susu Zhu, Fucheng Lin, Zhenzhu Su, Kangpei Yuan, Yiying Zhao, Yong He and Chu Zhang

Sensors 2018, 18(6), 1944; https://doi.org/10.3390/s18061944 - 15 Jun 2018

Cited by 23 | Viewed by 4213

Abstract

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis [...] Read more.

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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16 pages, 12971 KiB

Open AccessArticle

Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis

by Guangjun Qiu, Enli Lü, Huazhong Lu, Sai Xu, Fanguo Zeng and Qin Shui

Sensors 2018, 18(4), 1010; https://doi.org/10.3390/s18041010 - 28 Mar 2018

Cited by 49 | Viewed by 7457

Abstract

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing [...] Read more.

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn. Full article

(This article belongs to the Special Issue Infrared Sensors and Technologies)

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