Yibo Zhang

Event cameras are renowned for their high efficiency due to outputting a sparse, asynchronous stream of events. However, they are plagued by noisy events, especially in low light conditions. Denoising is an essential task for event cameras, but evaluating denoising performance is challenging. Label-dependent denoising metrics involve artificially adding noise to clean sequences, complicating evaluations. Moreover, the majority of these metrics are monotonic, which can inflate scores by removing substantial noise and valid events. To overcome these limitations, we propose the first label-free and non-monotonic evaluation metric, the area of the continuous contrast curve (AOCC), which utilizes the area enclosed by event frame contrast curves across different time intervals. This metric is inspired by how events capture the edge contours of scenes or objects with high temporal resolution. An effective denoising method removes noise without eliminating these edge-contour events, thus preserving the contrast of event frames. Consequently, contrast across various time ranges serves as a metric to assess denoising effectiveness. As the time interval lengthens, the curve will initially rise and then fall. The proposed metric is validated through both theoretical and experimental evidence. The codes are available at https://github.com/shicy17/AOCC.

Event cameras, which are highly sensitive to light intensity changes, often generate substantial noise during imaging. Existing denoising methods either lack the speed for real-time processing or struggle with dynamic scenes, mistakenly discarding valid events. To address these issues, we propose a novel dual-stage polarity-focused denoising (PFD) method that leverages the consistency of polarity and its changes within local pixel areas. Whether due to camera motion or dynamic scene changes, the polarity and its changes in triggered events are highly correlated with these movements, allowing for effective noise handling. We introduce two versions: PFD-A, which excels at reducing background activity (BA) noise, and PFD-B, which is designed to address both BA and flicker noise. Both versions are lightweight and computationally efficient. The experimental results show that PFD outperforms benchmark methods in terms of the SNR and ESR metrics, achieving state-of-the-art performance across various datasets. Additionally, we propose an FPGA implementation of PFD processes that handles each event in just 7 clock cycles, ensuring real-time performance. The codes are available at https://github.com/shicy17/PFD.

Light interference negatively impacts on frame-based visual tasks. Phenomena such as overexposure cause the loss of valuable information and reduce task execution efficiency. Event cameras are neuromorphic vision sensors that output sparse, asynchronous streams of events rather than frames. These cameras feature high temporal resolution, high dynamic range, and low power consumption. As a result, they are not susceptible to overexposure and motion blur, and they are able to recognize light interference such as strobe lights, stray lights, and reflections. However, event cameras are highly sensitive to light intensity changes, so light interference still affects event cameras as noise which easily alias with events triggered by environmental objects. Therefore, to reduce or eliminate the negative impact of light interference on event cameras, we systematically analyze the optical properties and event-triggering principles of these forms of light interference, and then propose ELIR (Event-based Light Interference Removal) method for removing light interference signals in event streams under static and dynamic scenes. The proposed method is validated in object detection tasks. Additionally, we launch the LIED datasets to evaluate the effect of light interference removal in event streams to assist with other studies in this field. Experimental results on the LIED datasets show that our proposed method can remove, on average, over 97% of light interference in static scenes, over 86% in dynamic scenes. Finally, the proposed method is verified on the object detection task, achieving an average PRE over 92%. The dataset is available at https://github.com/shicy17/LIED.

A multi-core fiber (MCF) provides a compact solution for three-dimensional (3D) shape measurement. In this Letter, an incremental shape measurement method for 3D paths is proposed, using an MCF based on fiber Bragg gratings (FBGs). A few FBG sets can iteratively provide plenty of strain information about the 3D path during navigation. The overall continuities of the curvature and torsion are improved based on intensive strain calculations. Meanwhile, the transformation matrix algorithm is used to reconstruct the shape of 3D paths. Dynamic measurement experiments of a seven-core fiber with two FBG sets are carried out to verify the incremental shape measurement method. This method shows a great performance of the different paths, with a maximum incremental position error of 4.68%.

Vibration measurement is an important part of monitoring the movement of objects and the working state of equipment. Non-contact vibration measurement can be achieved without installing a measuring device on the object to be measured, thus it has stronger applicability. However, traditional non-contact measurement methods, such as frame-based visual measurement, are prone to motion blur and difficult data processing. Event cameras encode the relative change of light intensity rather than the absolute value of the intensity recorded by conventional cameras, and this unique imaging mechanism makes them stand out with no motion blur, high temporal resolution and low power consumption. The displacement produced by the vibration of the object causes changes in light intensity, which are naturally captured by the event camera. Therefore, the use of event camera for vibration frequency measurement has inherent advantages. However, existing event-based methods cannot work under unstable lighting conditions, because changes in ambient light trigger events and interfere with the generation of events triggered by changes in light intensity produced by vibrations. Moreover, existing event-based computational methods do not model the mapping relationship between events and vibrations, the results of which are thus inaccurate. This article presents a method that utilizes laser-assisted illumination to help measure vibration frequencies. At the same time, a mixture Gaussian distribution is proposed to fit the frequency distribution. The experimental results show that the proposed method achieves accurate non-contact vibration frequency measurement of the object fixed on a vibrating table in the measurement range from 20 Hz to 300 Hz, with a relative error of 6.23 ‰, 4.03 ‰ and 5.45 ‰ under the ambient lighting of 0.01 lux, 55 lux and 3800 lux respectively. Furthermore, the measurement results of the vibration frequency of the loudspeaker show that the proposed method can measure the vibration in multiple directions.

The FBG-based embedded optical fiber 3D shape sensor (FBG-EOFSS) composed of several different materials finds potential applications in various industrial and medical applications. The strain loss during the strain transfer affects the change of the fiber Bragg grating (FBG) sensor’s wavelength which in turn deteriorate the position accuracy. In this paper, firstly, we analyzed the strain transfer in FBG-EOFSS and derived the optical-mechanical transformation equation. Then a strain transfer model combining mechanics, materials, and optics is developed based on the finite element analysis (FEA) method. The FEA simulation results show that the strain transfer rate decreases by 27.7%/mm with the increase of the adhesive thickness under FBG. The optimal average strain transfer rate is about 0.97 when the elastic modulus of the adhesive is in the range of 10 ⁹ Pa to 10 ¹⁴ Pa. Moreover, the effect of the strain transfer on the position accuracy is analyzed. The relative position error of 3D shape reconstruction increases by 8.4% with the strain transfer rate decreasing by 10%. Finally, an experimental system composed of the clamp, the displacement stage, and the FBG demodulator is developed. The strain sensitivity coefficients of bare FBGs and FBG sensors were tested quantitatively. The corresponding average strain transfer rate was calculated by the strain sensitivity coefficients. The results can potentially conduce to the design of 3D shape sensors and improve the positioning accuracy.

The harsh space environment brings great challenges to Fiber Bragg grating (FBG) sensors and FBG demodulation systems. The FBG demodulation system needs the redundancy design to improve reliability in the space environment. A high-reliable dual light sources multiplexing FBG demodulation system is proposed. It consists of two light sources, two 1 × 4 optical fiber couplers, four 1 × 3 optical fiber couplers, four photodetectors, and a signal processing circuit. The proposed FBG demodulation system eliminates single-point failures thoroughly and improves the FBG demodulation capability. The failure mode analysis is used for three different types of FBG demodulation systems. The results of failure mode analysis show that the proposed FBG demodulation system has higher reliability compared with the other two FBG demodulation systems in different failure cases

Micrometeoroid and orbital debris (MMOD) can cause serious impact damage to long-term on-orbit spacecraft. MMOD impact detection and location are essential to improve the safety and reliability of the on-orbit spacecraft. In this article, a combined artificial neural network (ANN) and Mahalanobis distance method for MMOD impact detection and location based on fiber Bragg grating (FBG) sensor network is proposed. The inputs of the proposed ANN are the Mahalanobis distances determined by the wavelength changes of the FBG sensors at impact points and reference points. A finite element model is developed to explore the relationship between the Mahalanobis distance and the actual distance, which proved to be nonlinear based on the finite element simulation results of MMOD impacts. Four FBG sensors were installed on the back of the test board and sampled at a frequency of 100 Hz using a four-channel FBG demodulator. A total of 360 impact experiments were carried out on this experiment system. The average location error of the proposed method is 0.89 cm, which is 39% and 31% lower compared with the Mahalanobis distance discriminant analysis method and the ANN method, respectively. The combined ANN and Mahalanobis distance method reduces the location errors and has higher accuracy of position prediction.

The shape sensing technology based on multi-core fiber is attractive for many applications. A particularly important problem needing to be addressed is minimizing possible errors in shape sensing. In this paper, one of sensing errors, namely the axial strain error of multi-core fiber in shape sensing is investigated. A model of the axial strain error is established with the theoretical analysis. Furthermore, a correction method for axial strain error using measured data obtained from fiber Bragg gratings sensors is developed. In order to validate the correction method, the twist experiment of a seven-core fiber is carried out. All calculated strain values are processed using curve fitting analysis. The characteristics of the corrected data fitting present good sinusoidal properties and confirm the theoretical analysis well. The results show that the mean R2 of the fitted strain values after the correction is 0.9578. The feasibility of the proposed method is verified by experimental results.