Oil Scale Profile Detection in Oil Pipeline Based on Improved Gamma-Ray Scanning Transmission Method

Chen, Zhiqiang; Chen, Jingjing; Shu, Shuangbao; Yu, Ziqiao; Zhang, Yuzhong; Tao, Xiaojie; Lang, Xianli

doi:10.1080/00295639.2022.2072660

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Compared with traditional neural networks, U 2 -Net can capture more contextual information, but the network is easy to lose important information with low frequency, which is especially obvious in the face of small target detection. When extracting buildings, the model does not pay enough attention to small buildings, which is easy to miss detection, and ignoring edge information leads to blurred building edges [7]. Therefore, this paper improves the building extraction method based on the U 2 -Net by adding building edge information data to the training data before they are input to the network, and incorporating the convolutional block attention module in the decoding stage of the network to improve the building extraction effect.…”

Section: Methodsmentioning

confidence: 99%

Building extraction from high resolution remote sensing images based on improved U2-Net

Huo

Xie²,

Gu³

2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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Buildings are one of the most important infrastructures in cities. Automatic extraction of buildings from high-resolution remote sensing imagery is of great significance for urban management and population estimation. Aiming at the problem that insufficient edge features and loss of details in the building extraction results, a network model combined with Canny edge detection and convolutional block attention module based on U 2 -Net was proposed in this paper. Adding the building edge feature map to the U 2 -Net to compensate for the problems of insufficient extracted building edge features and loss of detail. Using the convolutional block attention module to achieve effective feature extraction of buildings. Qualitative and quantitative analyses were performed on the WHU building datasets. The experimental results showed that improved U 2 -Net can accurately extract building regions from remote sensing images. And for the problem that deep learning network relies on training samples in the process of building extraction, this paper discusses the influence of the number of samples on the results. The experimental results showed that a reasonable setting of the number of building samples can improve the extraction accuracy of buildings, not that the greater the number of samples, the higher the accuracy.

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Section: Methodsmentioning

confidence: 99%

Building extraction from high resolution remote sensing images based on improved U2-Net

Huo

Xie²,

Gu³

2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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“…Ray radiation technology obtains the film thickness by detecting the attenuation of the ray intensity after penetrating the object. Despite that the ray radiation methods demonstrate the capacity for high-precision real-time film thickness measurement, the radioactive sources are expensive and highly hazardous [13][14][15]. Terahertz time-domain spectroscopy (THz-TDS) utilizes the time-of-flight (TOF) and resonance frequencies to acquire the thickness of the films.…”

Section: Introductionmentioning

confidence: 99%

Large-Area Thickness Measurement of Transparent Films Based on a Multichannel Spectral Interference Sensor

Huang,

Tu,

et al. 2024

Applied Sciences

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Thickness measurement of thin films is essential for quality control in the manufacturing process of the semiconductor and display industries. Real-time monitoring of film thickness during production is an urgent technical problem to be solved. In this study, a method for large-area thickness measurement of transparent films based on a multichannel spectral interference sensor is proposed. The sensor simultaneously acquires multichannel spectral interference signals through a combination of fan-out fiber optic bundles, detection probes, and an imaging spectrometer. The spectral data are calibrated and transformed into the wavenumber dimension, and then the power spectral density estimation method is used to demodulate the data frequency to swiftly derive the film thickness. The thickness measurement capacity of the proposed system is successfully validated on two standard film samples with a relative deviation of less than 0.38% and a relative standard deviation of less than 0.044%. The total spectral acquisition and calculation time for a single multichannel measurement was approximately 7.5 ms. The experimental results on polyimide films show that the measurement efficiency of the system is at least 4 times higher than that of the traditional system, indicating the potential of the multichannel spectral interference sensor for online monitoring in film production.

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Oil Scale Profile Detection in Oil Pipeline Based on Improved Gamma-Ray Scanning Transmission Method

Cited by 2 publications

References 21 publications

Building extraction from high resolution remote sensing images based on improved U2-Net

Building extraction from high resolution remote sensing images based on improved U2-Net

Large-Area Thickness Measurement of Transparent Films Based on a Multichannel Spectral Interference Sensor

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