Thermal Infrared Images Research Articles

Determination of Joint Defects in Copper Tube Induction Heating Brazing Area Using Infrared Thermal Image Based on CNN Algorithm

Determination of Joint Defects in Copper Tube Induction Heating Brazing Area Using Infrared Thermal Image Based on CNN Algorithm

Comparison of a Novel Periarticular Injection Technique with Intra-articular Injection of Platelet-rich Plasma for the Treatment of Osteoarthritis Joint Pain in Dogs

Canine osteoarthritis (OA) is characterized by degenerative changes in the joints resulting in pain and lameness. The study objective was to compare treatment of OA with a novel peri-articular injection (aqua-acupuncture/aqua-AP) technique using platelet-rich plasma (PRP), with standard intra-articular (IA) injection. Sixteen dogs diagnosed with OA in appendicular skeletal joints were enrolled and assigned to Test (n=8) or Control (n=8) treatment groups. Each subject received an injection at Week 0 and again at Week 4. Treatment outcomes assessed included goniometry (flexion and extension), infrared thermal imaging (IRTI), lameness grading scores and Canine Brief Pain Inventory (CBPI) questionnaire. The flexion and extension assessments had no statistically significant changes at 4 or 8-week assessment. Significant score reductions (improvement) were observed in both groups (p<0.001) for pain severity (2, 4, 8 weeks) and activity interference (2, 4, 8 weeks) for CBPI data with no significant difference between study groups concluded (p>0.05). Infrared thermal imaging demonstrated statistically significant (p<0.001), decreased temperature in the inflamed joints for both injection techniques, however, aqua-AP treatment was statistically significant at both 4 and 8 weeks while IA technique didn’t reach significance until 8 weeks post-treatment. Lameness scores had significant improvements at 4 and 8 weeks with no significant difference between groups. The findings suggest that PurePRP® treatment delivered with either the proposed aqua-AP or the standard IA injection may improve pain and thermal outcomes (decrease joint inflammation) with more rapid thermal improvement for aqua-AP. There were no clear effects on goniometry measurements. Further investigations with larger scale trials are warranted.

On‐Orbit Spatial Performance Characterization for Thermal Infrared Imagers of Landsat 7, 8, and 9, ECOSTRESS and CTI

AbstractIn this analysis of the spatial resolving power of thermal imagery products we focus on four satellite instruments that are used in research and applications, for example, to monitor land surface temperature and derive evapotranspiration. These are thermal imagers on Landsat 7, Landsat 8, and Landsat 9, as well as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). We compiled sets of close‐in‐time, day‐time images of bridges surrounded by open water bodies, captured by each of the satellite imagers during cloud‐free moments. Where possible, we also included some images captured by the Compact Thermal Imager (CTI), a technology demonstrator that was co‐located with ECOSTRESS on the International Space Station in 2019. Bridges were found to provide a sufficient thermal contrast with the water surface to quantify the line‐spread function of satellite‐based thermal products. The full‐width‐at‐half‐max of a gaussian beam model fitted to this transect quantifies the on‐orbit spatial resolution of different imagers. The results show some loss of spatial resolving power in the final product delivered to end‐users as compared to the at‐sensor characterization of spatial resolution. For Landsat 7, 8, and 9, the spatial resolution of the thermal bands is 1.5 times the ground sampling distance of 60 and 100 m respectively. For the ECOSTRESS the difference is up to twice the sampling distance of 78 by 69 m2. Since spatial resolution is a main driver for instrument design it is important to understand and communicate this discrepancy between pre‐flight design parameters and the characteristics of the surface imagery delivered to the user community. The goal of this research is to facilitate an improved fusion of current and future satellite observations into harmonized products with superior temporal and spatial characteristics.

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Water Deficit Diagnosis of Winter Wheat Based on Thermal Infrared Imaging

Field experiments were conducted to analyze the effectiveness of the crop stress index (CWSI) obtained by infrared thermal imaging to indicate crop water status, and to determine the appropriate CWSI threshold range for wheat at different growth stages. The results showed that the sensitivity of plant physiological parameters to soil water was different at different growth stages. The sensitivity of stomatal conductance (Gs) and transpiration rate (Tr) to soil water was higher than that of leaf relative water content (LRWC) and photosynthetic rate (Pn). The characteristics of plant physiology and biomass (yield) at each growth stage showed that the plant production would not suffer from drought stress as long as the soil water content (SWC) was maintained above 57.0% of the field water capacity (FWC) during the jointing stage, 63.0% of the FWC during the flowering stage and 60.0% of the FWC during the filling stage. Correlation analysis showed that the correlation of CWSI with Gs, Tr and Pn was lower than that with LRWC and SWC at the jointing stage. CWSI was extremely significantly negatively correlated with SWC and LRWC (p < 0.01), but significantly negatively correlated with Gs, Tr and Pn (p < 0.05). At the flowering stage, CWSI was extremely significantly negatively correlated with all physiological and soil parameters (p < 0.01). The regression analysis showed that the CWSI of winter wheat was correlated with biomass (grain yield) in a curvilinear relationship at each growth stage. When the CWSI increased to a certain extent, the biomass and yield showed a decreasing trend with the increase in CWSI. Comprehensive analysis of all indexes showed that CWSI can be used as a decision-making index to guide the water-saving irrigation of winter wheat, as long as the CWSI threshold of plants was maintained at 0.26–0.38 during the jointing stage, 0.27–0.32 during the flowering stage and 0.30–0.36 during the filling stage, which could not only avoid the adverse effects of water stress on crop production, but also achieve the purpose of water saving.

Retrieval of Plateau Lake Water Surface Temperature from UAV Thermal Infrared Data

The lake water surface temperature (LWST) is a critical parameter influencing lake ecosystem dynamics and addressing challenges posed by climate change. Traditional point measurement techniques exhibit limitations in providing comprehensive LWST data. However, the emergence of satellite remote sensing and unmanned aerial vehicle (UAV) Thermal Infrared (TIR) technology has opened new possibilities. This study presents an approach for retrieving plateau lake LWST (p-LWST) from UAV TIR data. The UAV TIR dataset, obtained from the DJI Zenmuse H20T sensor, was stitched together to form an image of brightness temperature (BT). Atmospheric parameters for atmospheric correction were acquired by combining the UAV dataset with the ERA5 reanalysis data and MODTRAN5.2. Lake Water Surface Emissivity (LWSE) spectral curves were derived using 102 hand-portable FT-IR spectrometer (102F) measurements, along with the sensor’s spectral response function, to obtain the corresponding LWSE. Using estimated atmospheric parameters, LWSE, and UAV BT, the un-calibrated LWST was calculated through the TIR radiative transfer model. To validate the LWST retrieval accuracy, the FLIR Infrared Thermal Imager T610 and the Fluke 51-II contact thermometer were utilized to estimate on-point LWST. This on-point data was employed for cross-calibration and verification. In the study area, the p-LWST method retrieved LWST ranging from 288 K to 295 K over Erhai Lake in the plateau region, with a final retrieval accuracy of 0.89 K. Results demonstrate that the proposed p-LWST method is effective for LWST retrieval, offering technical and theoretical support for monitoring climate change in plateau lakes.

A Study on Defect Detection of Dissimilar Joints in Cu-STS Tubes Using Infrared Thermal Imaging of Induction Heating Brazing

We proposed a novel detection method for identifying joint defects in the brazing process between copper tubes and stainless steel using a convolutional neural network (CNN) model. The brazing joints were created using high-frequency induction heating equipment, and infrared thermal imaging cameras were employed to capture the thermal data generated during the jointing process. The experiments involved 15.88 mm diameter copper tubes commonly used in plate heat exchangers, stainless-steel tubes, and filler metal containing 20% Ag. The thermal data were obtained with a resolution of 80 × 80 pixels per frame, resulting in 4796 normal joint data and 5437 defective joint data collected over 100 high-frequency induction-heating brazing experiments. A total of 10,233 thermal imaging data were categorized into 6548 training data, 1638 validation data, and 2047 test data for the development of the predictive model. We designed CNN models with varying hyperparameters, specifically the number of kernel filters and nodes, to evaluate their impact on detection performance. A comparative analysis revealed that a CNN model structure, exhibiting 98.53% accuracy and 99.82% recall on test data, was the most effective. The selected CNN-based defect prediction model demonstrated the potential of using CNN models to discern joint defects in tube configurations that are challenging to identify visually. This study opens avenues for applying CNN-based models for detecting imperfections in complex tube structures.

Exploring Video Denoising in Thermal Infrared Imaging: Physics-Inspired Noise Generator, Dataset, and Model.

We endeavor on a rarely explored task named thermal infrared video denoising. Perception in the thermal infrared significantly enhances the capabilities of machine vision. Nonetheless, noise in imaging systems is one of the factors that hampers the large-scale application of equipment. Existing thermal infrared denoising methods, primarily focusing on the image level, inadequately utilize time-domain information and insufficiently conduct investigation of system-level mixed noise, presenting the inferior ability in the video-recorded era; while video denoising methods, commonly applied to RGB cameras, exhibit uncertain effectiveness owing to substantial dissimilarities in the noise models and modalities between RGB and thermal infrared images. In sight of this, we initially revisit the imaging mechanism, while concurrently introducing a physics-inspired noise generator based on the sources and characteristics of system noise. Subsequently, a thermal infrared video denoising dataset consisting of 518 real-world videos is constructed. Lastly, we propose a denoising model called multi-domain infrared video denoising network, capable of concentrating features from the time, space, and frequency domains to restore high-fidelity videos. Extensive experiments demonstrate that the proposed method achieves state-of-the-art denoising quality and can be successfully applied to commercial cameras and downstream vision tasks, providing a new avenue for clear videography in the thermal infrared world. The dataset and code will be available.

Evaluation Classification of Weathering Degree of Stone Cultural Relics by Infrared Thermal Imaging

Evaluation Classification of Weathering Degree of Stone Cultural Relics by Infrared Thermal Imaging

Infrared medical thermography, medical applications, and its basic principles: A review

This narrative review aims to summarize in one paper all the known applications of infrared (IR) in medical thermography, medical applications, and the standardization protocols and basic conditions for thermal imaging in diagnostic medicine until December 2023. This technology primarily harnesses naturally emitted IR radiation from the subject's surface. These IR radiations emitted from the human body surface have been used in different fields of medical imaging to diagnose different kinds of diseases, disorders, and congenital anomalies and aid in medical therapy. Thermal imaging is a noninvasive, non-contact diagnostic technique used to measure the temperature of the human body. Therefore, IR thermal imaging has drawn significant attention and interest in recent years in a broad range of commercial applications due to its unique and superior properties. This commercial development has been supported by fundamental and applied research studies.

Global and Local Contrast Adaptive Enhancement Methods for Low-Quality Substation Equipment Infrared Thermal Images

Global and Local Contrast Adaptive Enhancement Methods for Low-Quality Substation Equipment Infrared Thermal Images

Anomaly Detection and Fault Classification of Printed Circuit Boards Based on Multimodal Features of the Infrared Thermal Imaging

Anomaly Detection and Fault Classification of Printed Circuit Boards Based on Multimodal Features of the Infrared Thermal Imaging

CA-MSPNet: Multi-Scale Module and Coordinate Attention Mechanism Strategy Combined with ProtoNet for Gearbox Fault Diagnosis Using Limited Infrared Thermal Images Under Variable Working Conditions

CA-MSPNet: Multi-Scale Module and Coordinate Attention Mechanism Strategy Combined with ProtoNet for Gearbox Fault Diagnosis Using Limited Infrared Thermal Images Under Variable Working Conditions

Unsupervised Feature-Preserving CycleGAN for Fault Diagnosis of Rolling Bearings Using Unbalanced Infrared Thermal Imaging Sample

Unsupervised Feature-Preserving CycleGAN for Fault Diagnosis of Rolling Bearings Using Unbalanced Infrared Thermal Imaging Sample

FSPnP: Plug-and-Play Frequency–Spatial-Domain Hybrid Denoiser for Thermal Infrared Image

FSPnP: Plug-and-Play Frequency–Spatial-Domain Hybrid Denoiser for Thermal Infrared Image

Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization of Traffic Scenes

Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization of Traffic Scenes

Multispectral Deep Neural Network Fusion Method for Low-Light Object Detection.

Despite significant strides in achieving vehicle autonomy, robust perception under low-light conditions still remains a persistent challenge. In this study, we investigate the potential of multispectral imaging, thereby leveraging deep learning models to enhance object detection performance in the context of nighttime driving. Features encoded from the red, green, and blue (RGB) visual spectrum and thermal infrared images are combined to implement a multispectral object detection model. This has proven to be more effective compared to using visual channels only, as thermal images provide complementary information when discriminating objects in low-illumination conditions. Additionally, there is a lack of studies on effectively fusing these two modalities for optimal object detection performance. In this work, we present a framework based on the Faster R-CNN architecture with a feature pyramid network. Moreover, we design various fusion approaches using concatenation and addition operators at varying stages of the network to analyze their impact on object detection performance. Our experimental results on the KAIST and FLIR datasets show that our framework outperforms the baseline experiments of the unimodal input source and the existing multispectral object detectors.

An enhanced multi-scale deep convolutional orchard capsule neural network for multi-modal breast cancer detection

Breast cancer is the second-leading cause of cancer death in women. Breast cells develop into malignant, cancerous lumps, the first signs of breast cancer. Breast cancer can be discovered by the automated diagnostic system when it is still too little to be found by conventional medical methods. Early breast cancers identified with automated screening and diagnosis technologies are generally treatable. This study proposes an enhanced multi-scale deep Convolutional Capsule Neural Network (CapsNet) optimized with Orchard Optimization Algorithm for breast cancer detection. The proposed system consists of preprocessing, feature extraction, segmentation, and classification process. Two input images are taken initially: the Breast Cancer Histopathology Images dataset and the Infrared Thermal Images dataset. The quality of the collected data is improved, and unwanted noises are removed. The features are extracted to segment the image to derive a Region of Interest for effectively segmenting the affected region. Finally, the images are classified as benign/malignant for histopathology images and healthy/cancer for thermal images. The proposed CapsNet is implemented in Python, run for 200 epochs, and compared with existing methods in terms of evaluation metrics. The result shows that the proposed CapsNet attained 99.74 % accuracy, 0.0482 binary entropy loss on the Breast Cancer Histopathology Image dataset and 97 % accuracy, 0.2081 binary entropy loss on the Infrared Thermal Images dataset while incrementing the epochs at each level.

MSEDNet: Multi-scale fusion and edge-supervised network for RGB-T salient object detection

RGB-T Salient object detection (SOD) is to accurately segment salient regions in both visible light images and thermal infrared images. However, most of existing methods for SOD neglects the critical complementarity between multiple modalities images, which is beneficial to further improve the detection accuracy. Therefore, this work introduces the MSEDNet RGB-T SOD method. We utilize an encoder to extract multi-level modalities features from both visible light images and thermal infrared images, which are subsequently categorized into high, medium, and low level. Additionally, we propose three separate feature fusion modules to comprehensively extract complementary information between different modalities during the fusion process. These modules are applied to specific feature levels: the Edge Dilation Sharpening module for low-level features, the Spatial and Channel-Aware module for mid-level features, and the Cross-Residual Fusion module for high-level features. Finally, we introduce an edge fusion loss function for supervised learning, which effectively extracts edge information from different modalities and suppresses background noise. Comparative demonstrate the superiority of the proposed MSEDNet over other state-of-the-art methods. The code and results can be found at the following link: https://github.com/Zhou-wy/MSEDNet.

A Feasibility Study of Thermal Infrared Imaging for Monitoring Natural Terrain—A Case Study in Hong Kong

The use of infrared thermography (IRT) technique combining other remoting sensing techniques such as photogrammetry and unmanned aerial vehicle (UAV) platforms to perform geotechnical studies has been attempted by several previous researchers and encouraging results were obtained. However, studies using time-lapse IRT survey via a UAV equipped with a thermal camera are limited. Given the unique setting of Hong Kong, which has a high population living in largely hilly terrain with little natural flat land, steep man-made slopes and natural hillsides have caused significant geotechnical problems which pose hazards to life and facilities. This paper presents the adoption of a time-lapse IRT survey using a UAV in such challenging geotechnical conditions. Snapshot and time-lapse IRT studies of a selected site in Hong Kong, where landslides had occurred were carried out, and visual inspection, photogrammetry, and IRT techniques were also conducted. 3D terrain models of the selected sites were created by using data collected from the photogrammetry and single (snapshot) and continuous monitoring (time-lapse) infrared imaging methods applied in this study. The results have successfully identified various thermal infrared signatures attributed to the existence of moisture patches, seepage, cracks/discontinuities, vegetation, and man-made structures. Open cracks/discontinuities, moisture, vegetation, and rock surfaces with staining can be identified in snapshot thermal image, while the gradient of temperature decay plotted in ln(T) vs. ln(t) enables quantifiable identifications of the above materials via time-lapse thermography and analysis.