Poisson distribution model is the basis of data analysis for GM-APD lidar, but it is only applicable to the mirror reflected target under ideal conditions, and cannot accurately describe the photon triggering process of actual GM-APD lidar detection. For the actual target with rough surface, the negative binomial distribution with M as the parameter can describe the photon distribution model more accurately. In order to solve this problem, this paper compares and analyzes Poisson distribution triggering model and the negative binomial distribution triggering model that conforms to the triggering situation of the echo signal of the target with rough surface, considering the differences in the triggering probability under different noise and signal levels. The results show that the trigger probability curve corresponding to the trigger model based on negative binomial distribution is lower in peak value, wider in bottom value and fatter overall than that of the trigger model based on Poisson distribution, and the difference between the two is more prominent under the conditions of low noise level and high signal level. This study has guiding significance for the signal extraction research based on different surface echoes.
KEYWORDS: Photons, Light scattering, Electric fields, Monte Carlo methods, Polarization, Mie scattering, Particles, Laser scattering, Scattering, Electromagnetic scattering theory
Traditional Monte Carlo method lacks the capability to trace and record polarization and phase information when simulating the transmission of photons. In this paper, based on Mie scattering theory and Electric Field Monte Carlo(EMC) method, we simulates the multiple scattering between photons and smoke particles and obtains the intensity and electric field information of backscattered photons, by calculating the rotation rules of local coordinate system and amplitude scattering matrix of photon. Simulations reveal that with an idealized point light source, backscattered photons concentrate into a speckle on the receiving surface, which expands as the scattering media thickens. The number of backscattered photons rapidly decreases over time, while their degree of polarization stabilizes within a specific range, forming a cross pattern that rotates based on the light's initial polarization. These findings offer valuable insights for developing anti-smoke interference imaging techniques.
Limited statistical frame number and strong backscatter interference from smoke result in a photon-starved regime, severely limiting the depth imaging capability of array Gm-APD lidar in smoky environment. Here, we propose a depth image estimation algorithm that can significantly improve the integrity of targets in dense smoke environments when signal photons are starved. At the signal level, the algorithm improves the accuracy of extracting signal photons by constructing multi-scale superpixels. At the image level, using edge information of depth images at different scales to guide and fill the original scale depth image achieves efficient noise reduction and improves target integrity. It has been successfully demonstrated under different attenuation lengths and statistical frame numbers. Compared with other state-of-the-art methods, the proposed algorithm has maximum target recovery and structural similarity, especially for the attenuation length (AL) is 3.6 and statistical frame number is 1500 (imaging time is 75ms). This study is of great significance for the fast depth imaging of dynamic targets by array Gm-APD lidar in dense smoke environments.
Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm- APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification dataset.Following that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research.
The non-uniform distribution of smoke and laser spot seriously limits the imaging ability of single-photon lidar through smoke. To this end, based on the collision theory between photons and smoke particles, this paper establishes the time-domain distribution model of scattered photons (Gamma), which considers the lidar system parameters and the characteristics of smoke particles. The shape parameter and inverse scale parameter are defined as the maximum scattering number K and the average scattering number β, respectively. The indoor test effectively illustrates the estimation ability of the model for smoke. The parameter estimation results show that the average scattering number β and the maximum scattering number K increase linearly with the increase of attenuation coefficient. This study is of great significance for the suppression of smoke backscattering and is expected to improve the weather adaptability of single-photon lidar.
LiDAR echo intensity information can reflect the reflection characteristics of the target surface, and can be used as an important data source in the aspects of LiDAR point cloud image vision, classification and feature extraction. Geiger mode avalanche photodiode (Gm-APD) has the ability of single photon detection and high range sensitivity, and is widely used in the field of lidar. The number of statistics is often taken as the target intensity information obtained. In order to make the intensity image accurately reflect the reflection characteristics of the target surface, a kind of intensity information correction method of Gm-APD lidar is proposed. By eliminating the distortion caused by the detection model and target distance of the detector, the average reflectivity estimation error can be increased from 51.97% to 8.86%. Aiming at Gm-APD lidar, the determination method of parameters in parameter estimation method is systematically described in this paper. On this basis, the calibration of the laser emitter can improve the uniformity of the target, and the standard deviation is increased from 1.1818 to 0.0050. The proposed scheme can provide a reliable data source for target recognition, classification and feature extraction based on Gm-APD intensity image.
Aiming at the problem that the background noise mixed in the target echo will affect the calculation of the target polarization degree when the traditional polarization detection system obtains the target polarization degree, based on the polarization Gm-APD detection model, a set of target echo polarization correction method is proposed. The target is imaged in a xenon lamp environment, the influence of target attitude and polarization angle on detection is explored, and the polarization imaging results are analyzed. The results show that the polarization system has a significant effect on metal materials with low surface roughness. When circularly polarized light is incident, the echo trigger probability of the metal material reaches a peak at the polarization angle of 135°. The greater the incident angle, the greater the echo depolarization and the lower the trigger probability. By inverting the distribution of echo photons, the number of background noise photons in the echo and the number of target echo photons can be obtained respectively, and a more accurate correction of the polarization degree of the target echo can be obtained. For metal materials, when the target attitude angle is 30°, the target polarization before and after correction are 0.47 and 0.57 respectively, and the target echo polarization after correction is 7% higher than that without correction. This research work provides experimental support for the effective detection and target detection of GM-APD lidar in the daytime.
Bi-directional reflection distribution function (BRDF) is a common method to study the laser scattering characteristics of targets, and it is an important parameter for the theoretical demonstration of laser active detection, target recognition and classification. Scholars at home and abroad have proposed many mature BRDF models to describe the scattering characteristics of different targets. However, almost all of these models do not take into account the effect of incident wavelength on scattering characteristics. In addition, limited by the frequency modulation range of the laser, the existing BRDF measurement devices cannot obtain the BRDF data of the target at any wavelength, which restricts the application of the existing BRDF model. In view of this limitation, a method is proposed to calculate the unknown wavelength BRDF data using the BRDF measurement data of known wavelengths. Firstly, based on the Kirchhoff approximation theory, the spatial distribution of the scattered light field of the metal aluminum target at any wavelength was simulated and analyzed. Secondly, the error of the theoretical simulation model was analyzed through the experimental data. Finally, the BRDF data at any wavelength were calculated using the simulation data and the experimental data with known wavelengths. The final results showed that at the 1064nm wavelength, the RMSE value of the calculated data obtained by this method is 0.3553, which is 0.2233 smaller than the RMSE value of the simulation data.This method is effective in calculating the BRDF of metal aluminum targets at different wavelengths.
In this paper, three algorithms are proposed to restore the fog-containing relative intensity image of lidar based on the atmospheric scattering model and dark channel prior theory. The algorithm was evaluated by analyzing the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the two data sets, including the fog-free relative intensity images and the fog-containing relative intensity images and standard fog-free relative intensity images. The experimental results show that the PSNR of the two groups of data can be improved by the three algorithms to varying degrees, and the highest PSNR can reach 35.6%. The structure similarity SSIM was significantly improved, and the effect was up to three orders of magnitude.
Edges are critical important for the visual appearance of images. The traditional denoising algorithms are difficult to preserve the edges of the image while removing the noise of ICCD sensing image. At the same time, it is difficult to eliminate the problems of image darkness and low resolution caused by uneven illumination. This paper proposes a multilevel filtering image denoising algorithm based on edge information fusion. The target edges detection of the image after non-local means (NL-means) filtering is carried out based on the eight-direction Sobel operator. In order to filter the false edge points and residual noise, an adaptive threshold is determined according to the mean and variance of the eight neighborhood pixels of the detected pixel. Meanwhile, homomorphic filtering is used to enhance the image contrast and uniformity. By comparing the pixel values of the edge image and the homomorphic filtered image, the final denoised image is obtained by fusing the two images. The results indicate that, compared with the traditional algorithms, the edge preserving ability of the proposed algorithm is improved by more than 20%, and the denoising ability is improved by 63.5% for building target. For specific targets (vehicle), the results demonstrate that the proposed algorithm have the maximum edge preserving index and contrast, and the minimum non-uniformity. This algorithm lays a foundation for target segmentation and recognition.
When using Gm-APD Lidar for depth imaging through realistic fog, the echo signal of the target is submerged in the background noise due to the strong absorption and scattering characteristics of the fog particles, resulting in serious defect of the recovered depth image of the target. To solve this problem, this paper proposes a dual-parameter estimation algorithm based on continuous wavelet transform (CWT) and maximum likelihood estimation (MLE) to improve the accuracy of fog signal estimation. Then the target and the fog signal are separated by estimating the fog signal of each pixel. Finally, the depth image of the separated target is processed by cross pixel complement and median filtering algorithms to improve the integrity of the target image. The experimental results show that, compared with the traditional algorithm, the target recovery of the reconstructed image is improved by 0.337, and the relative average ranging error is reduced by 0.3897. This research improves the weather adaptability of Gm-APD Lidar.
Gm-APD arrays lidar has the advantages of long imaging distance, small volume and low power consumption. Because of its unique high resolution three-dimensional range profile, it is expected to solve the problems of UAV safe flight, autonomous obstacle avoidance and so on. In this paper, according to the dynamic imaging requirements of UAV lidar, a joint image stabilization control algorithm of adaptive Kalman filter and PID is proposed to suppress the disturbance of UAV platform to lidar system and make the laser beam point to the target stably. The vibration test experiment of Gm- APD lidar system is made. Under the condition of horizontal amplitude 5mm and frequency 15Hz sine wave disturbance, the gyro drift is less than 1.7 °/ s, and the target drift is no more than 4 pixels. It is proved that Gm-APD lidar can be applied in the field of UAV safe flight.
In the process of underwater lidar wake detection, the multipath effect leads to pulse stretching of the echo signal, which is a distinguishing feature to distinguish the wake echo signal. In order to explore the factors that affect the pulse stretching of the echo signal, this paper convolves the instantaneous echo energy of the bubbles at different distance layers with the transmitted pulse, and establishes a distance layer summation model(DLSM) of underwater bubbles. This paper analyzes the effect of bubble density on the backscattering coefficient of the bubbles and the pulse width of the echo signal, and introduces the attenuation length of the water and the multipath effect. The experimental results show that when the attenuation length increases from 1.2 to 2.0, the half-peak width of the echoes of the bubbles increases by 1.3ns. However, when the attenuation length increases from 1.5 to 2.2, the echo pulse width of the strong backscatter target decreases by 0.5ns. The thickness of the bubbles increases from 2cm to 5cm, the peak shifts by 0.4ns, and the echo pulse width increases by 0.6ns. The simulation model and experimental results provide an effective basis for distinguishing ship wakes and strong backscattering targets, and have an important role in improving wake detection and recognition capabilities.
Aiming at the problem that the Geiger-mode Avalanche Photodiode (GM-APD) is susceptible to background noise and the detection effect decreases during the day, based on the polarization GM-APD detection model, a set of GM-APDbased polarized lidar imaging experimental equipment is proposed. Using this equipment to image the target in the simulated sunlight environment, the effect of polarization detection on the echo triggering performance was studied. The results show that, compared with the non-polarized system, the polarized system reduces the impact of noise on target detection, improves the image quality, and reduces the false alarm probability of the image. When the laser single pulse energy is 400nJ, and the polarization angle is 135° , the trigger probability of the metal plate is increased by 10.5%, and its false alarm probability is reduced by 4.6%; the trigger probability of the rough wood board is reduced by 15.2%, and its false alarm probability is reduced 8.9%.Due to the deterioration of imaging results caused by background noise, it is proposed to use polarization degree images for further background filtering to extract a more complete target contour. This research work provides experimental support for the effective detection of GM-APD lidar during the day.
The Geiger mode Avalanche Photo Diode (APD) array lidar is a non-scanning lidar, which has a small volume, fast imaging speed and high sensitivity. In the paper, the 3D target detection of Geiger mode APD array lidar image is studied. Geiger mode APD array lidar has great noise in the process of imaging due to its imaging characteristics. The paper analyzes its noise characteristics and decomposes the noise into four parts: environment noise, loss noise, internal noise and crosstalk noise. According to the noise characteristics, the paper simulated the Geiger-mode APD array lidar imaging. And based on this, the target detection algorithm was studied. The paper proposes a filtering method based on the KNN classification and combine an improved loop filtering algorithm to preprocess the image. And then an adaptive superposition algorithm is proposed to fuse the preprocessed multi-frame image. Testing the target detection algorithm on five image data captured by the Geiger mode APD array lidar, the medium-scale and small-scale targets can be detected in 20 frames. The largescale targets can be detected in 50 frames, and long-distance targets can be detected in 100 frames.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.