Characteristics Of Thermal Images Research Articles

Quantitative analysis method for infrared characterization of coal damage under load based on image enhancement and pixel extraction

To explore the quantification method of infrared thermal image characteristics in the coal loaded-damage process and better reflect the infrared precursor information and damage evolution of coal, in this paper, a new index based on the color extraction of thermal image pixel points, infrared high-temperature anomaly area ratio (IHAR), is proposed to quantitatively analyze the infrared thermal image characteristics in combination with image processing technology. The results show that: after the denoising and enhancement based on the double-domain decomposition and improved CLAHE algorithm, the infrared thermal image effect is improved; the change of IHAR index is controlled by stress, with significant characteristics of stages, which are synchronized and consistent with the infrared thermal image sequence; The precursors of IHAR show significant increase and fluctuation, and the time point is around 60% to 65% of the peak stress; compared with other infrared indicators, IHAR is better in terms of stage, easy identification of precursors, and validity of damage characterization, which can effectively reflect the evolution process of coal loaded damage, and provide early warning information for coal destabilization monitoring.

Asymmetric cross-modal activation network for RGB-T salient object detection

RGB-thermal salient object detection (RGB-T SOD) has unique advantages in terms of handling challenging scenes with cluttered backgrounds, low illumination, and low contrast. However, because they do not consider the significant differences between different imaging mechanisms and inherent characteristics of thermal images, existing RGB-T SOD methods are generally unable to handle diverse feature fusion demands and may yield unsatisfactory performance. To overcome this problem and achieve more effective RGB-T SOD, we propose an asymmetric cross-modal activation network to exploit the interactions of modality-specific features based on an asymmetric feature fusion strategy. Specifically, a two-stream asymmetric feature aggregation encoder module is proposed to fuse multimodality features adaptively and extract complementary information. The self-attention of multimodality features is leveraged to guide cross-modal interactions, which can propagate long-range contextual dependencies and extract effective saliency cues. Furthermore, a multitask decoder is proposed to achieve SOD and thermal image reconstruction in a unified framework. Salient objects can be located and segmented accurately based on reconstructed high-resolution feature representations. Extensive experiments on public RGB-T and RGB-D SOD datasets demonstrate the superiority of the proposed network and ablation experiments highlight the effectiveness of each component. Our code and saliency maps are available at: www.github.com/xanxuso/ACMANet.

Characteristics of thermal images of the mammary gland and of performance in sows differing in health status and parity.

Precision livestock farming can combine sensors and complex data to provide a simple score of meaningful productivity, pig welfare, and farm sustainability, which are the main drivers of modern pig production. Examples include using infrared thermography to monitor the temperature of sows to detect the early stages of the disease. To take account of these drivers, we assigned 697 hybrid (BHZP db. Viktoria) sows to four parity groups. In addition, by pooling clinical findings from every sow and their piglets, sows were classified into three groups for the annotation: healthy, clinically suspicious, and diseased. Besides, the udder was thermographed, and performance data were documented. Results showed that the piglets of diseased sows with eighth or higher parity had the lowest daily weight gain [healthy; 192 g ± 31.2, clinically suspicious; 191 g ± 31.3, diseased; 148 g ± 50.3 (p < 0.05)] and the highest number of stillborn piglets (healthy; 2.2 ± 2.39, clinically suspicious; 2.0 ± 1.62, diseased; 3.91 ± 4.93). Moreover, all diseased sows showed higher maximal skin temperatures by infrared thermography of the udder (p < 0.05). Thus, thermography coupled with Artificial Intelligence (AI) systems can help identify and orient the diagnosis of symptomatic animals to prompt adequate reaction at the earliest time.

Maximizing Self-Supervision From Thermal Image for Effective Self-Supervised Learning of Depth and Ego-Motion

Recently, self-supervised learning of depth and ego-motion from thermal images shows strong robustness and reliability under challenging scenarios. However, the inherent thermal image properties such as weak contrast, blurry edges, and noise hinder to generate effective self-supervision from thermal images. Therefore, most research relies on additional self-supervision sources such as well-lit RGB images, generative models, and Lidar information. In this letter, we conduct an in-depth analysis of thermal image characteristics that degenerates self-supervision from thermal images. Based on the analysis, we propose an effective thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency. The proposed method shows outperformed depth and pose results than previous state-of-the-art networks without leveraging additional RGB guidance.

Visible-Thermal Image Object Detection via the Combination of Illumination Conditions and Temperature Information

Object detection plays an important role in autonomous driving, disaster rescue, robot navigation, intelligent video surveillance, and many other fields. Nonetheless, visible images are poor under weak illumination conditions, and thermal infrared images are noisy and have low resolution. Consequently, neither of these two data sources yields satisfactory results when used alone. While some scholars have combined visible and thermal images for object detection, most did not consider the illumination conditions and the different contributions of diverse data sources to the results. In addition, few studies have made use of the temperature characteristics of thermal images. Therefore, in the present study, visible and thermal images are utilized as the dataset, and RetinaNet is used as the baseline to fuse features from different data sources for object detection. Moreover, a dynamic weight fusion method, which is based on channel attention according to different illumination conditions, is used in the fusion component, and the channel attention and a priori temperature mask (CAPTM) module is proposed; the CAPTM can be applied to a deep learning network as a priori knowledge and maximizes the advantage of temperature information from thermal images. The main innovations of the present research include the following: (1) the consideration of different illumination conditions and the use of different fusion parameters for different conditions in the feature fusion of visible and thermal images; (2) the dynamic fusion of different data sources in the feature fusion of visible and thermal images; (3) the use of temperature information as a priori knowledge (CAPTM) in feature extraction. To a certain extent, the proposed methods improve the accuracy of object detection at night or under other weak illumination conditions and with a single data source. Compared with the state-of-the-art (SOTA) method, the proposed method is found to achieve superior detection accuracy with an overall mean average precision (mAP) improvement of 0.69%, including an AP improvement of 2.55% for the detection of the Person category. The results demonstrate the effectiveness of the research methods for object detection, especially temperature information-rich object detection.

Infrared Detection and Thermoelectric Coupling Simulation Analysis of 220 kV Insulator Abnormal Heating

Heat and pollution flash caused by surface contamination are one of the common defects of insulators. If not discovered and eliminated in time, it will cause the deterioration of the insulator and seriously affect the service life of the insulator. If we can grasp the temperature distribution characteristics of the insulator in the state of contamination, the infrared temperature measurement can be used to quickly find and eliminate defects in a timely manner. This paper introduces a large area heating defect of porcelain post insulator and composite suspension insulator in 220kV equipment area, whose temperature distribution showed the rule of overheating at the high-voltage end and the temperature gradually decreasing toward the low-pressure end. Based on the field infrared temperature measurement and thermal image characteristics, it preliminarily analyzed that the heating was caused by insulator contamination and damp. Then, combined with pollution sampling, three-dimensional model establishment and thermoelectric coupling simulation calculation, the correctness of the above conclusions was verified, and preventive measures and suggestions were put forward.

Defect detection and evaluation of ultrasonic infrared thermography for aerospace CFRP composites

The ultrasonic infrared thermography Non-destructive Testing is introduced for detecting the impact damage of a CFRP specimen for Unmanned Aerial Vehicles. The characteristics of thermal images with damage are particularly analyzed. A Local Binary Fitting (LBF) model based on a non-Gaussian kernel function is used to segment the defect edge. In view of the discontinuity of defect in thermal images due to multilayered structure of composite materials, defect merging algorithms are proposed including time domain and space domain methods by using a few thermal images, and the defect geometric distortion during camera imaging is also compensated. The defect in the composite material can be quantitatively analyzed after the defect reconstruction. The experimental result has shown that the proposed algorithm can effectively detect and evaluate the impact damage of thermal images and the accuracy of quantitative assessment is correspondingly increased.