Mr. Fei Chen Profile

Fei Chen

at Beijing Institute of Technology

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Publications (3)

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Proceedings Article | 20 January 2023 Paper

VOC gas leakage detection using infrared image and convolutional neural networks

C. Zhang, F. Chen, L. Su, B. Yang, Z. Hu, W. Hong

Proceedings Volume 12555, 125550D (2023) https://doi.org/10.1117/12.2651541

KEYWORDS: Convolution, Feature extraction, Convolutional neural networks, Gases, Mid-IR, Infrared imaging, Sensors, Infrared radiation, Infrared detectors, Embedded systems

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Leakage of volatile organic compounds (VOC) gas is one of the main sources of air pollution, and it poses a great threat to health and safety in many ways. Optical gas imaging (OGI) technique utilizes mid-wave infrared camera to visualize VOC gas and helps people observe the leakage of VOC gas. In this paper, we propose a novel method that utilizes deep learning technique and convolutional neural networks to detect the leakage of VOC gas from single-frame mid-wave infrared image. The proposed method consists of three components: color transformation pre-processing unit, feature extraction networks, and single-stage object detection sub-networks. Location-aware deformable convolution, which adjusts its sampling grid to fit the ever-changing shape of VOC gases, is employed for better feature extraction. Besides, a new loss function called leakage center loss is introduced to estimate where leakage comes from, and it forces the network to pay more attention to leakage center where the density of VOC gases is higher than dissipated parts. The proposed method is evaluated using a self-collected dataset where thousands of gas images are captured and annotated. Experimental results show that location-aware deformable convolution contributes to around 7%mAP improvement, while leakage center loss contributes to around 4% mAP improvement. Finally, our method achieves 81%mAP, which is better than existing general-purpose object detection methods. By simplifying the network architecture, our proposed method can also be implemented on embedded system for handheld VOC leakage detection devices.

Proceedings Article | 6 September 2019 Presentation + Paper

Motion robust imaging photoplethysmography in defocus blurring

Yuheng Wu, Lingqin Kong, Fei Chen, Yuejin Zhao, Liquan Dong, Ming Liu, Mei Hui, Xiaohua Liu, Cuiling Li, Weijie Wang

Proceedings Volume 11137, 1113705 (2019) https://doi.org/10.1117/12.2527655

KEYWORDS: Photoplethysmography, Video, Blood, RGB color model, Motion models

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Non-contact, imaging photoplethysmography (IPPG) uses video sequence to measure variations in light absorption, caused by blood volume pulsations, to extract cardiopulmonary parameters including heart rate (HR), pulse rate variability, and respiration rate. Previous researches most focused on extraction of these vital signs base on the focus video, which require a static and focusing environment. However, little has been reported about the influence of defocus blur on IPPG signal’s extraction. In this research, we established an IPPG optical model in defocusing motion conditions. It was found that the IPPG signal is not sensitive to defocus blur by analysis the light intensity distribution in the defocus images. In this paper, a real-time measurement of heart rate in defocus and motion conditions based on IPPG was proposed. Automatically select and track the region of interest (ROI) by constructing facial coordinates through facial key points detection, obtained the IPPG signal. The signal is de-noised to obtain the spectrum by the wavelet filtering, color-distortion filter (CDF) and fast Fourier transform (FFT). The peak of the spectrum is corresponded to heartbeats. Experimental results on a data set of 30 subjects show that the physiological parameters include heart rate and pulse wave, derived from the defocus images captured by the IPPG system, exhibit characteristics comparable to conventional the blood volume pulse (BVP) sensor. Contrast experiment show that the difference between the results measured by both methods is within 3 beat per minute (BPM). This technology has significant potential for advancing personal health care and telemedicine in motion situation.

Proceedings Article | 24 October 2017 Paper

Using a photograph containing the sun to measure the rotation and slope of terrain

Xia Wang, Zimu Wu, Fei Chen, Weiqi Jin

Proceedings Volume 10458, 1045817 (2017) https://doi.org/10.1117/12.2285009

KEYWORDS: Photography, Sun

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The measurement accuracy of the rotation and slope of terrain is a critical factor for the performance of some scene simulation application systems. As the main observed illuminant outdoors, the sun can furnish a rich source of information about the scene. In this paper, we analyze the relationship between the coordinates of the sun in photographs and the zenith and azimuth angles of the camera. By fitting a model of the predicted sun position to the pinhole camera model, we show how to measure the rotation and slope of terrain by using a photograph containing the sun. We test our methods on a sequence of photographs with known camera parameters, and obtain deviation of less than 1.7° for the rotation angle and 2.2° for the slope angle of the terrain. The measuring method by using a photograph containing the sun can be useful for a variety of practical applications such as navigation, time measurement and camera calibration.

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