•Dorper x Santa Inês crosses heat tolerance does not decrease by the presence of wool.•Infrared thermography as good non-invasive tool to assess changes in body temperature.•Thermoregulatory ...efficiency of Santa Inês sheep and crosses with Dorper are similar.
This study aimed to assess the thermoregulatory responses of Santa Inês (SI), Dorper x Santa Inês (CH) and White Dorper x Santa Inês sheep (CW) to direct solar radiation in Southeast Brazil. Thirty adult non-pregnant and non-lactating Santa Inês (SI) hair ewes and their crosses with Dorper (hair ewes) and White Dorper (wool ewes) were allocated into three groups n = 10 and exposed to continuous solar radiation for three consecutive days. Ocular and surface temperatures, measured by infrared thermography, the rectal temperature, respiratory rate and sweating rate were collected at 7:00, 13:00 and 20:00 h. During the experiment, the black globe temperature reached a peak at 13:00 h, reaching mean values of 43.5 °C ± 0.45 °C, representative of severe discomfort for sheep. All genotypes showed an increase in surface temperature, reaching the maximum value at 13:00 h. The wool White Dorper x Santa Inês showed significantly higher surface temperature (dorsal, ventral, and shoulder) than the other genotypes. All the genotypes showed similar rectal temperature increases, peaking at 13:00 h, with values close to 39.4 °C ± 0.12 °C. At 20:00 h, all the genotypes decreased the rectal temperature (RT), albeit not reverting to the 7:00 h values. Changes in ocular temperature values mirrored the RT. All the genotypes presented high levels of evaporative heat loss. Even though all breeds significantly increased the respiratory rate, Santa Inês exhibited significantly higher values (146 bpm) than the others (112 and 117 for CH and CW, respectively). The sweating rate was very high in all genotypes, without differences among them, and exhibiting the same trend with a maximum value at 20:00 h. This behavior reflects the continuous effort to lose heat during the day, despite the decrease in black globe temperature. This study revealed analogous thermoregulatory responses among genotypes groups studied. The three genotypes showed similar heat tolerances, albeit presenting different thermogenesis and thermolysis dynamics, as evidenced by the maintenance of rectal temperatures within physiological limits even when subjected to intense high solar radiation.
•A new method for detecting mastitis in dairy cows was proposed.•The method combines two different temperature difference detection methods.•The method presented reduces the influence of external ...factors and improves detection accuracy.•You Only Look Once v5 algorithm has performed better in detecting the position of cows' key parts.•The algorithm is used to locate cows' eyes and udders and automatically detect mastitis.
Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to the dairy industry. Many attempts have been made to develop a detection method for mastitis using thermal infrared thermography. However, the use of this detection technique to determine the health of the cow's udder is susceptible to external factors, resulting in inaccurate detection of dairy cow mastitis. Therefore, this study explored a new and comprehensive detection method of dairy cow mastitis based on infrared thermal images. This method combined the left and right udder skin surface temperature (USST) difference detection method with the ocular surface temperature and USST difference detection method with improvements. The effect of external factors on dairy cow USST was effectively reduced. In addition, after comparing different target localisation algorithms, this paper used the You Only Look Once v5 (YOLOv5) deep learning network model to obtain the temperature information of eyes and udders, and mastitis detection of dairy cows was performed. A total of 105 dairy cows passing through a passage were randomly selected from the thermal infrared video and detected by the new and comprehensive detection method, and the results of cow mastitis detection were compared with somatic cell count. The results showed that the accuracy, specificity, and sensitivity of mastitis detection were 87.62, 84.62, and 96.30%, respectively. Using the YOLOv5 deep learning network model to locate the key parts of the cow had a good effect, with an average accuracy of 96.1%, and an average frame rate of 116.3f/s. The detection accuracy of dairy cow mastitis by deep learning technology combined with the detection method in this paper reached 85.71%. The results showed that the new and comprehensive detection method based on infrared thermal images can be used for the detection of dairy cow mastitis with high detection accuracy. This method can reduce the influence of external factors and can be integrated into the automatic identification system of dairy mastitis based on YOLOv5 to realise on-site monitoring of dairy mastitis.
This paper proposes an approach for the reliable identification of subsurface damages in thermal images of concrete structures. The work explores how to mitigate false positives in subsurface ...delamination segmentation using thermal and visible images. The methodology employs a few-shot learning method, specifically the Siamese Neural Network (SNN), to assess the similarity between corresponding multimodal regions. The findings indicate that leveraging similarities between visible and thermal images reduces false positives and improves the segmentation model’s precision by 3.6%, eliminating 351 false positives. These results enhance the reliability of semi-automatic models for detecting subsurface delamination using infrared thermography, benefiting infrastructure maintenance and encouraging the research and development of compact and reliable automation models that integrate civil engineering, nondestructive testing, and artificial intelligence domains.
•Few-shot learning approach to mitigate false positives in delamination segmentation.•Evaluation with 500 pairs of infrared and visible images from concrete infrastructure.•Multimodal SNN integration and filtering enhance the precision of segmentation model.
This paper describes a real-time, high-performance deep-learning network to segment internal damages of concrete members at the pixel level using active thermography. Unlike surface damage, the ...collection and preparation of ground truth data for internal damage is extremely challenging and time consuming. To overcome these critical limitations, an attention-based generative adversarial network (AGAN) was developed to generate synthetic images for training the proposed internal damage segmentation network (IDSNet). The developed IDSNet outperforms other state-of-the-art networks, with a mean intersection over union of 0.900, positive predictive value of 0.952, F1-score of 0.941, and sensitivity of 0.942 over a test set. AGAN improves 12% of the mIoU of the IDSNet. IDSNet can perform real-time processing of 640 × 480 × 3 sizes of thermal images with 74 frames per second due to its extremely lightweight segmentation network with only 0.085 M total learnable parameters.
•Real-time deep-learning internal damage segmentation network using thermography.•Internal damage segmentation network (IDSNet) proposed for concrete members.•Attention-based GAN (AGAN) for generating artificial training data for IDSNet.•AGAN synthetic data improves the 12% mean intersection over the union of IDSNet.•IDSNet outperforms state-of-the-art models with a 90% mIoU.
Photovoltaic (PV) solar energy recorded an exponential growth, in worldwide scale, over the last decade. Inevitably, mature PV markets are becoming highly competitive, boosting the need for research ...and development (R&D) on efficiency and reliability optimization, maintenance and fault diagnosis of key components, such as the PV modules. Indeed, a significant number of studies and technical papers have been published up today, based on an extensive feedback from both laboratory and real (field) investigations of faults and advanced diagnosis applications, especially for crystalline silicon (c-Si) PV modules. Undoubtedly, such experience is of particular interest for current PV plant operators, future investors, maintenance engineers and the R&D sector of PV industry. However, up today, such research, published in the form of reports, technical papers or even books, remains mostly dispersed and unclassified. This paper represents a comprehensive effort to review and highlight recent advances, ongoing research and future prospects, as reported in the literature, on the classification of faults in c-Si PV modules and advanced diagnosis in the field, by means of the increasingly popular method of infrared thermal (IRT) imaging. In particular, the first main part of this paper, reviews the characteristics of the most common fault types of operating PV modules, in terms of electrical and thermal response. Then, the second part gives a thorough review of recently published research, as well as the state-of-the-art, in the fields of IRT-based fault diagnosis and thermal image processing. On the basis of these two individual though supplementary review parts, an overview table is presented, followed by a discussion on the future prospects and challenges, towards the understanding and diagnosis of faults and their propagation in operating PV modules.
•By now, Non-Destructive techniques (NDTs) are the best to infrastructure inspection.•InfraRed Thermography (IRT) is one of the best NDTs to infrastructure inspection.•Exhaustive review of the most ...recent and important IRT post-acquisition procedures.•Qualitative and quantitative modes are the typical IRT post-acquisition approaches.•Robustness of IRT to face the greatest challenges in infrastructure inspection.
The different thermal behaviour between defects and unaltered zones allows the detection and thermal characterisation of superficial, and subsuperficial defects, which must be considered when maintaining a structure in optimal conditions. InfraRed Thermography is among the most appropriate Non-Destructive techniques to measure these thermal behaviours, represented on temperature maps of the infrastructure analysed by thermal images, regardless the size of the structure. In addition, InfraRed Thermography is also used for the thermal characterisation of structures for such important purposes as the energy study of buildings. Proof of the importance of InfraRed Thermography in infrastructure inspections are the continuous developments of new thermal image processing algorithms, where the post-processing stage is widely used to improve the inspection performance. In this work, an exhaustive review is performed regarding the most recent and important practical thermographic procedures for infrastructure applications, focusing on the post-acquisition stage, due to the lack of an in-depth analysis regarding the most recent and used algorithms. Specifically, the theory of these thermal image processing techniques is described, classifying them according to the corresponding theoretical post-acquisition approach used: qualitative and/or quantitative analysis. In addition, a discussion based on the advantages and disadvantages of each thermal data processing technique is performed, as well as a description of the latest IRT works related to each. Ending with a series of conclusions, this review paper confirms the maturity of InfraRed Thermography to face the greatest challenges in infrastructure inspection, although it also mentions the limitations to overcome and the future trends to follow.
•Use of a novel ultrasonic-assisted in-house developed setup for skull bone grinding.•Determination of the effect of varying process parameters on temperature rise during grinding.•Development of a ...hybrid model for predicting tissue damage during bone grinding.•Finite element simulation to measure the thermogenesis and osteonecrosis depth.
The aim of the study was to develop a novel automated setup for bone grinding to limit the temperature to below 43 °C. The feasibility of using ultrasonic actuation during bone osteotomy was explored with different machining variables, such as rotational speed, feed rate and ultrasonic frequency, in terms of the criterion variable (i.e., temperature). A thermal dose model based on the CEM43 °C and the Arrhenius model was developed for the prediction of tissue damage during bone grinding. CEM43 °C is a normalizing method to convert the time-temperature relationship into an equivalent number of minutes at 43 °C. For every degree rise in temperature above 43 °C, the cell viability significantly increased. The temperature generated during bone grinding was measured with an infrared thermography technique. The increase in temperature above threshold levels of 43 °C and 47 °C may harm the bone tissues and cause thermogenesis and osteonecrosis, respectively. A finite-element simulation was conducted to visualise the spatial and temporal distribution of temperature on the bone surface after bone grinding. Furthermore, simulation results were used to measure the depth of thermogenesis and osteonecrosis at the grinding site. Evaluation of the optimised set of bone grinding process parameters was supported with analysis of variance at the 95% confidence level.
•The number of the factors that affect the skin temperature (Tsk) in humans is tremendously large.•This review proposes a comprehensive classification in three primary groups: environmental, ...individual and technical factors.•Further research is necessary to delimit the unspecified influence of most of the factors and to improve this classification.
Body temperature is one of the most commonly used indicators of health status in humans. Infrared thermography (IRT) is a safe, non-invasive and low-cost technique that allows for the rapid and non-invasive recording of radiating energy that is released from the body. IRT measures this radiation, directly related to skin temperature (Tsk) and has been widely used since the early 1960s in different areas. Recent technical advances in infrared cameras have made new human applications of IRT (beyond diagnostic techniques) possible. This review focuses on the lack of comprehensive information about the factors influencing the use of IRT in humans, and proposes a comprehensive classification in three primary groups: environmental, individual and technical factors. We aim: to propose a common framework for further investigations; to reinforce the accuracy of human IRT; to summarise and discuss the results from the studies carried out on each factor and to identify areas requiring further research to determine their effects on human IRT.
