scholarly journals A Thermal Infrared and Visible Images Fusion Based Approach for Multitarget Detection under Complex Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Xinnan Fan ◽  
Pengfei Shi ◽  
Jianjun Ni ◽  
Min Li
Keyword(s):  
Thermal Infrared ◽  
Basic Belief ◽  
Measured Signal ◽  
Complex Environment ◽  
Distance Distribution Function ◽  
Detection Approach ◽  
Efficiency And Effectiveness ◽  
Visible Images ◽  
Belief Theory ◽  
Update Process

Multitarget detection under complex environment is a challenging task, where the measured signal will be submerged by noise. D-S belief theory is an effective approach in dealing with Multitarget detection. However, there are some limitations of the general D-S belief theory under complex environment. For example, the basic belief assignment is difficult to establish, and the subjective factors will influence the update process of evidence. In this paper, a new Multitarget detection approach based on thermal infrared and visible images fusion is proposed. To easily characterize the defected heterogeneous image, a basic belief assignment based on the distance distribution function of heterogeneous characteristics is presented. Furthermore, to improve the discrimination and effectiveness of the Multitarget detection, a concept of comprehensive credibility is introduced into the proposed approach and a new update rule of evidence is designed. Finally, some experiments are carried out and the experimental results show the efficiency and effectiveness of the proposed approach in the Multitarget detection task.

scholarly journals Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

2021 ◽  
Vol 13 (9) ◽  
pp. 1852
Author(s):  
Yiren Wang ◽  
Dong Liu ◽  
Wanyi Xie ◽  
Ming Yang ◽  
Zhenyu Gao ◽  
...  
Keyword(s):  
Infrared Image ◽  
Thermal Infrared ◽  
Field Of View ◽  
Detection Scheme ◽  
Detection Approach ◽  
Cloud Characteristics ◽  
Formation And Evolution ◽  
Fish Eye ◽  
Infrared Channel

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation.

Thermal Infrared Polarimetric Sensor for Automated Detection of Oil Spills

2017 ◽  
Vol 2017 (1) ◽  
pp. 2017402
Author(s):  
David B. Chenault ◽  
Justin P. Vaden ◽  
Douglas A. Mitchell ◽  
Erik D. Demicco
Keyword(s):  
Early Detection ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Thermal Infrared ◽  
Automated Detection ◽  
Unmanned Aerial Systems ◽  
Detection Approach ◽  
Thermal Cameras ◽  
Aerial Systems

One of the most effective ways of minimizing oil spill impact is early detection. Effective early detection requires automated detection that relies as little as possible on an operator and can operate 24/7. A new and innovative optical detection system exploits the polarization of light, the same physics used to reduce glare through the use of polarized glasses but in the thermal infrared (TIR) portion of the optical spectrum. Measuring the polarization of thermally emitted radiation from an oil spill enhances the detection over conventional thermal cameras and has the potential to provide automated day / night monitoring and surveillance. The sensors developed thus far are relatively small and inexpensive and can be easily mounted in areas that need monitoring and installed in unmanned aerial systems (UAS). Since the sensor is adapted from a conventional TIR camera, thermal imagery as currently used is collected in addition to the polarimetric imagery to further improve the detection performance. Lens options enable wide area coverage at shorter ranges and higher resolution at longer ranges from the camera position. A TIR Polarimetric camera was tested at Ohmsett to establish performance under a variety of conditions. The Polarimetric camera was tested during the day and at night, under several different wave conditions generated in the wave tank, and with oil of different compositions and thicknesses. The imagery collected was analyzed to establish the contrast improvement through the polarimetric properties of the oil and to assess the automation of the detection process. In this poster, the sensor and test setup will be briefly described with detailed description of the results and the potential of this detection approach for automated detection.

scholarly journals Factors Influencing Temperature Measurements from Miniaturized Thermal Infrared (TIR) Cameras: A Laboratory-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8466
Author(s):  
Quanxing Wan ◽  
Benjamin Brede ◽  
Magdalena Smigaj ◽  
Lammert Kooistra
Keyword(s):  
Surface Temperature ◽  
Laboratory Experiments ◽  
Environmental Parameters ◽  
Thermal Infrared ◽  
Environmental Research ◽  
Measured Signal ◽  
Water Vapor Absorption ◽  
Multiple Sensors ◽  
Actual Field ◽  
Sensor Temperature

The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor’s actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research.

scholarly journals A Lightweight Deep Learning-Based Pneumonia Detection Approach for Energy-Efficient Medical Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bandar Almaslukh
Keyword(s):  
Deep Learning ◽  
Energy Efficient ◽  
Random Search ◽  
Diagnostic System ◽  
Quality Of Healthcare ◽  
Fine Tuning ◽  
Medical Systems ◽  
Feature Extraction Method ◽  
Detection Approach ◽  
Efficiency And Effectiveness

Early detection of pneumonia disease can increase the survival rate of lung patients. Chest X-ray (CXR) images are the primarily means of detecting and diagnosing pneumonia. Detecting pneumonia from CXR images by a trained radiologist is a challenging task. It needs an automatic computer-aided diagnostic system to improve the accuracy of diagnosis. Developing a lightweight automatic pneumonia detection approach for energy-efficient medical systems plays an important role in improving the quality of healthcare with reduced costs and speedier response. Recent works have proposed to develop automated detection models using deep learning (DL) methods. However, the efficiency and effectiveness of these models need to be improved because they depend on the values of the models’ hyperparameters. Choosing suitable hyperparameter values is a critical task for constructing a lightweight and accurate model. In this paper, a lightweight DL approach is proposed using a pretrained DenseNet-121-based feature extraction method and a deep neural network- (DNN-) based method with a random search fine-tuning technique. The DenseNet-121 model is selected due to its ability to provide the best representation of lung features. The use of random search makes the tuning process faster and improves the efficiency and accuracy of the DNN model. An extensive set of experiments are conducted on a public dataset of CXR images using a set of evaluation metrics. The experiments show that the approach achieved 98.90% accuracy with an increase of 0.47% compared to the latest approach on the same dataset. Moreover, the experimental results demonstrate the approach that the average execution time for detection is very low, confirming its suitability for energy-efficient medical systems.

scholarly journals A New Real-Time Path Planning Method Based on the Belief Space

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Yu-xin Zhao ◽  
Xin-an Wu ◽  
Yan Ma
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Basic Belief ◽  
Complex Environment ◽  
New Approach ◽  
Time Path ◽  
Local Path Planning ◽  
Simulation Results ◽  
Local Path ◽  
Belief Model

A new approach of real-time path planning based on belief space is proposed, which solves the problems of modeling the real-time detecting environment and optimizing in local path planning with the fusing factors. Initially, a double-safe-edges free space is defined for describing the sensor detecting characters, so as to transform the complex environment into some free areas, which can help the robots to reach any positions effectively and safely. Then, based on the uncertainty functions and the transferable belief model (TBM), the basic belief assignment (BBA) spaces of each factor are presented and fused in the path optimizing process. So an innovative approach for getting the optimized path has been realized with the fusing the BBA and the decision making by the probability distributing. Simulation results indicate that the new method is beneficial in terms of real-time local path planning.

scholarly journals Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2264
Author(s):  
Sara González-Pérez ◽  
Daniel Perea Ström ◽  
Natalia Arteaga-Marrero ◽  
Carlos Luque ◽  
Ignacio Sidrach-Cardona ◽  
...  
Keyword(s):  
Diabetic Foot ◽  
Focal Plane ◽  
Gradient Descent ◽  
Low Cost ◽  
Thermal Infrared ◽  
Dice Coefficient ◽  
Visible Spectra ◽  
Visible Images ◽  
The Subject ◽  
And Performance

This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms’ performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions.

High-resolution topographic SEM and radiation damage: The rationale for using low beam voltage

1992 ◽  
Vol 50 (2) ◽  
pp. 1278-1279
Author(s):  
James Pawley ◽  
David Joy
Keyword(s):  
High Resolution ◽  
Imaging Techniques ◽  
Morphological Structure ◽  
Radiation Sensitivity ◽  
Beam Specimen ◽  
Measured Signal ◽  
Practical Consideration ◽  
Interaction Volume ◽  
Impact Area ◽  
Signal Contrast

The scanning electron microscope (SEM) builds up an image by sampling contiguous sub-volumes near the surface of the specimen. A fine electron beam selectively excites each sub-volume and then the intensity of some resulting signal is measured and then plotted as a corresponding intensity in an image. The spatial resolution of such an image is limited by at least three factors. Two of these determine the size of the interaction volume: the size of the electron probe and the extent to which detectable signal is excited from locations remote from the beam impact area. A third limitation emerges from the fact that the probing beam is composed of a number of discrete particles and therefore that the accuracy with which any detectable signal can be measured is limited by Poisson statistics applied to this number (or to the number of events actually detected if this is smaller). As in all imaging techniques, the limiting signal contrast required to recognize a morphological structure is constrained by this statistical consideration. The only way to overcome this limit is to increase either the contrast of the measured signal or the number of beam/specimen interactions detected. Unfortunately, these interactions deposit ionizing radiation that may damage the very structure under investigation. As a result, any practical consideration of the high resolution performance of the SEM must consider not only the size of the interaction volume but also the contrast available from the signal producing the image and the radiation sensitivity of the specimen.

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