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Visual information can be easily obtained nowadays with more access to various photographic facilities. Appropriate logical analysis and processing of massive visual information could apply to flexible environment pollution detection. In this paper, we present a visual-based non-contact sensing method to detect the low frequency vibration pollution in daily life. The edge points of vibrating objects with more vibration information are extracted using bilateral filtering and Sobel operator. The Sobel operator weights the influence of the pixel position, which make the edge feature extraction effect better and the edge feature obtained more obvious. After that, the continuous frames of the vibration objects with edge information are transformed into several one-dimensional pixel level signals as the input of multi-scale network, which also improves the generalization ability. Experiments on daily objects under low frequency vibration are conducted. The detection accuracy is above 99%, which proves the reliability of the proposed method for the detection of low frequency vibration circumstance pollution.

In low-frequency vibration measurement field, optical dynamic 3-D measurement based on camera-projector system is a novel approach. In this system, linear sinusoidal grating is the most common projection mode, which can meet the requirements of some vibration measurement. However, the dynamic measurement range of amplitude using the combination of linear grating and Fourier transform profilometry (FTP) is limited in some vibration measurement environments, because the linear fringe is periodic. To solve the problem, this article proposes a low-frequency vibration measurement method based on the concentric-circle grating camera-projector system and FTP. The center of the circle is traced dynamically and the coordinate system is transformed with the center of the circle as the origin. The phase distribution of concentric-circle grating is obtained by coordinate system transformation and FTP. By exploring the relationship between phase and relative height, the time-varying curve of the object surface height is recovered, which contains the vibration information. The experiments compare the measurement ability of the concentric-circle and linear grating. The results show that the linear grating and concentric-circle grating with the similar amplitude resolution have different amplitude measurement range. In this experiment, the maximum measurement range of linear grating is about 2.4 mm, while that of concentric-circle grating is more than about 3.9 mm. It means that the concentric-circle grating projection method can measure a large dynamic range of amplitude under the condition that the center of the circle can be traced, and solve the problem of amplitude limitation of linear grating in the use of FTP.

Accurately detecting man-made intrusion from different events is of great significance for distributed optical fiber sensing perimeter security system. Most traditional algorithms lack the ability to reject various events of unknown class which are mainly from natural disturbance, and greatly decline the accuracy of intrusion recognition in field application. In order to solve this problem, we proposed a novel robust intrusion event recognition scheme based on convolutional prototype network (CPL), which realized end-to-end feature extraction and recognition based on the similarity of intrusion signals by integrating relevant variables of prototype learning into the training process of multiscale convolutional neural network (MSCNN) as trainable parameters, and had the ability to recognize and reject the unknown disturbance events. In field experiments, the average recognition accuracy of intrusion events as known class can reach 84.67%, with the rejection rate of disturbance events as unknown class is about 83.75%, which ensure the accuracy of intrusion events monitoring in complex field environments. And the recognition response time is about 17 ms, which also meets the need of real-time monitoring.

Intrusion monitoring based on optical fiber distributed sensing is a pattern recognition problem of target events. Due to the addition of uncontrollable unknown events in practical applications, the open-set problem brought about greatly reduces the accuracy of traditional events recognition methods. In order to solve this problem, this paper design a deep metric-learning network, combined with Recurrent Plot (RP) coding to improve the accuracy of target events recognition in an open environment. In this paper, the RP algorithm is used to encode the intrusion signals into images that reflect the signal’s motion. The deep metric-learning network is used to project the image into the feature space. And feature centers of each class can be calculated rely on the training samples. So, the network identifies the intrusion event according to the distance between the test sample and the feature center. By setting the appropriate threshold, the samples whose distance exceeds the threshold are identified as unknown class to solve the open-set problem. In the experiments, the dual Mach-Zehnder interferometry (DMZI) distributed optical fiber perimeter system is built to collect event signals. For 7 known event classes the highest identification accuracy can reach 99.7%. After adding 3 unknown event classes, the accuracy rate can exceed 93.3% with appropriate threshold. And the total response time is only 0.4 s. This novel method guarantees the accuracy of target identification under the introduction of unknown events and improves the robustness of the distributed optical fiber perimeter security system.

In order to enhance the image quality of multi-reflective 3D scenes during machine vision analysis, an active self-adaptive illumination system to solve the overexposure and underexposure problems is introduced. Based on projector-camera system, adaptive illumination was achieved by modulating the projection brightness at each pixel to be proportional to the pixel brightness of the target scenes, which should firstly establish the accurate pixel correspondence between the projector and camera. In this paper, the performance of adaptive illumination is improved by optimizing the correspondence stage compared with previous research. Different grating patterns with different frequency components are projected and compared to acquire the most effective correspondence, which improves the accuracy of corresponding depth map and the experiment results can achieve higher lighting accuracy reducing the number of iterations as well as the computing time. The image quality indexes, image contrast and number of saturated or dark pixels, are combined to analyze and judge the optimized lighting result. The results demonstrate that the developed technology in this research can achieve a rate of 20 frames per second by using a standard commercial CCD (charge coupled device) camera, where it can run in real time and becomes a viable solution for the industry.

A centimeter-sized bearing fault probe based on dual-fiber Bragg grating vibration sensing is proposed. The probe can provide multi-carrier heterodyne vibration measurements based on swept source optical coherence tomography technology and the synchrosqueezed wavelet transform method to obtain a wider vibration frequency response range and collect more accurate vibration data. For the sequential characteristics of bearing vibration signals, we propose a convolutional neural network with long short-term memory and transformer encoder. This method is proven in bearing fault classification under variable working conditions, and the accuracy rate reaches 99.65%.

Low frequency vibration monitoring has significant implications on environmental safety and engineering practices. Vibration expressed by visual information should contain sufficient spatial information. RGB-D camera could record diverse spatial information of vibration in frame images. Deep learning can adaptively transform frame images into deep abstract features through nonlinear mapping, which is an effective method to improve the intelligence of vibration monitoring. In this paper, a multi-modal low frequency visual vibration monitoring system based on Kinect v2 and 3DCNN-ConvLSTM is proposed. Microsoft Kinect v2 collects RGB and depth video information of vibrating objects in unstable ambient light. The 3DCNN-ConvLSTM architecture can effectively learn the spatial-temporal characteristics of muti-frequency vibration. The short-term spatiotemporal feature of the collected vibration information is learned through 3D convolution networks and the long-term spatiotemporal feature is learned through convolutional LSTM. Multi-modal fusion of RGB and depth mode is used to further improve the monitoring accuracy to 93% in the low frequency vibration range of 0–10 Hz. The results show that the system can monitor low frequency vibration and meet the basic measurement requirements.

In order to modify the local luminance heterogeneity of target object image, restrain the phenomenon of overexposure and preserve enough original image information, we analyzed the adaptability of the adjustable projection which is applied to exposure compensation. In this paper, a novel iterative method by using the improved algorithm for absolute center moment (IACM) is proposed. A projector-camera system with the iterative algorithm is built to adjust the adaptive projection according to the inversion theory, the differences of depth information and the illumination intensity. Among them, the role of depth information is correspond to different light intensity at different heights. Through the experiment, the result shows that the camera could achieve uniform luminance and the index convergence is evaluated by IACM. This system can output the image with better definition based on iterative stop criterion, thereby the number of iterations is limited and objectively countable. This technique is suitable for industrial measurement, defect detection and quality control in real-time measurement.

The ability of distributed Bragg reflector (DBR) fiber laser sensor in dual-frequency ultrasound (DFUS) measurement has been demonstrated. The influences of DFUS pressure on fiber grating laser sensor were theoretically analyzed, considering the effects of relative distance of the ultrasound field and the amplitude of the DFUS on the degrees of birefringence modulation. In experiment, the birefringence in sensing fiber was modulated by ultrasound signals at 3 MHz and 5 MHz, and the test distance and driving voltage were set respectively according to the ultrasound frequency. Agreement between theoretical and experimental results was obtained for ultrasound wave propagating from different distances (40 mm to 10 mm) and different DFUS driving voltage (5 V to 20 V). The results demonstrate that the DBR fiber grating laser acoustic sensor has a multi-frequency ultrasound recognizable ability, offering a potential for ultrasound medicine and biology

An illumination optimisation technique applied to multi-reflective 3-D machine vision based on a projector-camera system is introduced, in which the projector plays a key role to compensate for surface reflectance at each pixel to be inversely proportional to the brightness of the pixel under ambient light. The adaptive illumination technology was achieved by iterations emphasising different illumination intensities according to different surface orientations and requiring an accurate correspondence between the projector pixels and the camera pixels. In order to establish the most effective correspondence to prepare for subsequent adaptive illumination, 4 kinds of grating patterns, including sinusoidal, rectangular, triangular, and dual-frequency sinusoidal grating patterns, were projected and compared. The iterations were halted when an optimally lit scene was obtained; the further experiments under weak and strong light searched for the best method of illumination optimisation and confirmed the reliability of the adaptive illumination. The proposed optimisation design could run in real time and became a viable solution for industry.

For establishing an accurate and reliable distributed optical fiber perimeter security system, this paper proposes a novel abnormity detection solution to security using Recurrent Plot (RP) and deep learning technology. Take advantage of the temporal correlation of intrusion signals, we encode the sensing signals into two-dimensional images through the RP algorithm. The RP algorithm can extract the motion characteristics of the signal from the complex time series, and it is robust to instrument noise. These encoded image signatures can reveal the deeper temporal correlation of the intrusion signals’ motion. After that, Inception network can adaptively extract the features of these images to complete the accurate identification of a series of noisy intrusion signals. We conducted experiments on three most frequent natural events and three representative man-made intrusion events, including heavy rain, light rain, wind blowing, treading, slapping, and impacting. The results show that the detection accuracy has reached 99.7%. This method can achieve 0.35 s real-time detection in the online detection of abnormal events while ensuring accuracy, providing a new intrusion pattern identification idea for perimeter security.

As a type of pollution, low-frequency vibrations in the environment have a negative effect on human life. To monitor pollution without extra complications, we propose a novel method based on visual information perception and neural network analysis. Image frame sequences can capture the state of objects, and changes in pixel values at salient vibration points may indirectly reflect ambient vibrations. In this paper, a test environment is established to simulate the influence of vibrations on daily life. A CCD camera is used to continuously sample objects via image frame sequences to monitor potential vibration pollution in the current environment through image processing and neural network analysis. During the image processing stage, a combination of image filtering and edge extraction can accurately locate the test objects’ contours. During the low-frequency vibration monitoring stage, the neural network analyzes the changes in pixel values at the contour points to monitor ambient vibration pollution. The network combines local and global features for vibration frequency classification and prediction. The proposed method's superiority is verified by the test results and a comparison of four different networks. The results demonstrate that this method accurately locates salient vibration points to monitor ambient vibration pollution.

This letter reports an analysis of the potential of distributed Bragg reflector (DBR) fiber laser sensor designed to monitor the force orientation information, and with the birefringence characteristics of optical fiber, enabling the measurement of a directional lateral force applied to the fiber. In system design, to interrogate birefringence variation of the sensor according to the change of applied force angles, a reference lasing signal was employed following the results of an analysis using heterodyning theory. Agreement between theoretical and experimental results was obtained for a constant lateral force loading from different directions corresponding to the variation of sensitivity in beat signals. The design created in this letter is based on fundamental principles and generic, thus suitable for use with any DBR fiber laser structure for vector sensor applications.

Based on the study of self-Q-switching of a short cavity erbium-doped distributed Bragg reflector (DBR) fiber laser in time domain, the influence of pressure disturbance on the self-Q-switched output is analyzed. In the experiment, the erbium-doped DBR fiber laser (EDFL) was encapsulated with epoxy resin and curing agent to protect and sensitize the fiber. The EDFL sends out self-Q-switched laser pulses when the pump power of the 980 nm laser source is more than 110 mW. The external pressure is exerted on the DBR fiber and increased gradually from 0 g (0 N) to 120 g (1.176 N) in 10 g unit with the pump power fixed at 360 mW, and the following approximately linear changes of self-Q-switched laser pulses outputs are observed by the oscilloscope: the peak voltage of output pulses reduces from 12.59 mV to 1.883 mV; the pulse repetition rate reduces from 125.0 kHz to 66.67 kHz; and the pulse width increases from 0.4083 μs to 1.28 μs.