Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Lü, Hao; Qingying, Li; Pan, Weichen; Yao, Rao; Liu, Senyun

doi:10.1049/ipr2.12662

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…There have been some attempts to incorporate neural networks to predict ice accretion and ice thickness (Kreutz et al, 2019). Hao et al (Hao et al, 2023) employed temperature elevation data obtained from thermal infrared imager to deduce ground ice thickness via back propagation neural networks. They collected 500 thermal images as a dataset from an experimental work.…”

Section: Infrared Ice-detectionmentioning

confidence: 99%

Conceptual Design of Cost-Effective Ice Detection System Based on Infrared Thermography

Yousuf¹,

Khawaja²,

Virk³

2023

Preprint

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Section: Infrared Ice-detectionmentioning

confidence: 99%

Conceptual Design of Cost-Effective Ice Detection System Based on Infrared Thermography

Yousuf¹,

Khawaja²,

Virk³

2023

Preprint

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“…For each spectral band dataset, the information that it carries is different, as human eyes can only perceive part of the spectral band. In addition, owing to the limitation of imaging technology, there is currently no sensor device that can acquire all spectral band data [1][2][3]7]. For example, visible light imaging devices can be used to obtain rich visual information of objects such as texture, color, and shape from visible light spectrum bands that human eyes can perceive, whereas thermal infrared imaging devices can be used to obtain visual information of objects such as brightness, shape and contour from thermal infrared spectrum bands that human eyes cannot perceive.…”

Section: Of 19mentioning

confidence: 99%

“…In actual outdoor surveillance videos, when under low illumination at night and insufficient illumination in the daytime, the use of only visible light would easily result in missed detection and false detection of pedestrian objects, which cannot satisfy monitoring requirements throughout the day and thus cannot guarantee the security of people's lives and property [1][2][3][4]. Meanwhile, in terms of traffic safety and autonomous driving, video surveillance is also playing an increasingly important role in low illumination at night and insufficient illumination in the daytime [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, in terms of traffic safety and autonomous driving, video surveillance is also playing an increasingly important role in low illumination at night and insufficient illumination in the daytime [5,6]. With the advancement of technology, a variety of sensor devices have emerged, such as speed dome cameras, box cameras and high-definition sensors for use in the daytime, and thermal infrared, mid-wave infrared, and long-wave infrared sensors for use at night and in bad weather [1][2][3]7,8]. In order to satisfy all-day monitoring needs, it is necessary for different types of sensors to work together.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian Detection Method Based on Two-Stage Fusion of Visible Light Image and Thermal Infrared Image

et al. 2023

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Pedestrian detection has important research value and practical significance. It has been used in intelligent monitoring, intelligent transportation, intelligent therapy, and automatic driving. However, in the pixel-level fusion and the feature-level fusion of visible light images and thermal infrared images under shadows during the daytime or under low illumination at night in actual surveillance, missed and false pedestrian detection always occurs. To solve this problem, an algorithm for the pedestrian detection based on the two-stage fusion of visible light images and thermal infrared images is proposed. In this algorithm, in view of the difference and complementarity of visible light images and thermal infrared images, these two types of images are subjected to pixel-level fusion and feature-level fusion according to the varying daytime conditions. In the pixel-level fusion stage, the thermal infrared image, after being brightness enhanced, is fused with the visible image. The obtained pixel-level fusion image contains the information critical for accurate pedestrian detection. In the feature-level fusion stage, in the daytime, the previous pixel-level fusion image is fused with the visible light image; meanwhile, under low illumination at night, the previous pixel-level fusion image is fused with the thermal infrared image. According to the experimental results, the proposed algorithm accurately detects pedestrian under shadows during the daytime and low illumination at night, thereby improving the accuracy of the pedestrian detection and reducing the missed rate and false rate in the detection of pedestrians.

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“…Existing research has suggested that this method exhibits high accuracy in measuring the thickness of defects. Hao et al [21] used an infrared thermal imaging system that can convert infrared radiation temperature. Moreover, they extracted pixel-level temperature attenuation features using a theoretical model and polynomial fitting of time and temperature.…”

Section: Introductionmentioning

confidence: 99%

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Gou

Qingying

Yao

et al. 2023

Meas. Sci. Technol.

Self Cite

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Active infrared thermography has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis algorithm with sliding-window mechanism and the 1DResnet50-CBAM (One Dimensional - Residual Neural Network - Convolutional Block Attention Module) model was proposed for the reconstructed image sequence of active infrared thermography to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using principal component analysis algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional (3D) defect data were transformed into one-dimensional (1D) temporal infrared thermal signal. Moveover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion principal component analysis algorithm with sliding-window mechanism and 1DResnet50-CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and quantitative analysis using infrared thermal detection technology.

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Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Cited by 9 publications

References 44 publications

Conceptual Design of Cost-Effective Ice Detection System Based on Infrared Thermography

Conceptual Design of Cost-Effective Ice Detection System Based on Infrared Thermography

Pedestrian Detection Method Based on Two-Stage Fusion of Visible Light Image and Thermal Infrared Image

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

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