KEYWORDS: Detection and tracking algorithms, LIDAR, Signal attenuation, Visibility through fog, Visibility, Shape analysis, Sensors, 3D image reconstruction, Single photon, Single photon detectors, Fourier transforms, Reconstruction algorithms
Fog is a difficult medium to image through using Single-Photon Avalanche Diode (SPAD) based Light Detection and Ranging (LiDAR) systems because of its light scattering properties. Scattering significantly decreases the signal-to-noise ratio of photon returns, making it difficult to reconstruct meaningful images for target detection. In this paper, an image feature-based approach for reconstructing SPAD LiDAR images of a single target is proposed. Geometric characteristics of the target are used in the algorithm to differentiate between target and background photon returns. Combinations of different features such as Fourier shape descriptors and apparent target size are used to improve performance. To validate the algorithm, a 32×32 silicon SPAD array Flash LiDAR system operating at 532nm is used for collecting images through fog. Simple geometric shapes are placed indoors in a dark tunnel 44.6m from the sensor with fog decreasing the visibility in steps down to 12m. The proof-of-concept algorithm achieves good localization performance at a fog level of 1.4 attenuation lengths.
Navigating through fog plays a vital part in many remote sensing tasks. In this paper, we propose an Expectation- Maximization (EM) algorithm for fitting a mixture of lognormal and Gaussian distributions to the probability distributions of photon returns for each pixel of a 32x32 Single Photon Avalanche Diode (SPAD) array image. The distance range of the target can be determined from the probability distribution of photon returns by modeling the peak produced due to fog scattering with a lognormal distribution while the peak produced by the target is modeled by a Gaussian distribution. In order to validate the algorithm, 32x32 SPAD array images of simple shapes (triangle, circle and square) are imaged at visibilities down to 50.8m through the fog in an indoor tunnel. Several aspects of the algorithm performance are then assessed. It is found that the algorithm can reconstruct and distinguish different shapes for all of our experimental fog conditions. Classification of shapes using only the total area of the shape is found to be 100% accurate for our tested fog conditions. However, it is found that the accuracy of the distance range of the target using the estimated model is poor. Therefore, future work will investigate a better statistical mixture model and estimation method.
A real time program is implemented to classify different model airplanes imaged using a 32x32 SPAD array camera in time-of-flight mode. The algorithm uses random feature extractors in series with a linear classifier and is implemented on the NVIDIA Jetson TX2 platform, a power efficient embedded computing device. The algorithm is trained by calculating the classification matrix using a simple pseudoinverse operation on collected image data with known corresponding object labels. The implementation in this work uses a combination of serial and parallel processes and is optimized for classifying airplane models imaged by the SPAD and laser system. The performance of different numbers of convolutional filters is tested in real time. The classification accuracy reaches up to 98.7% and the execution time on the TX2 varies between 34.30 and 73.55 ms depending on the number of convolutional filters used. Furthermore, image acquisition and classification use 5.1 W of power on the TX2 board. Along with its small size and low weight, the TX2 platform can be exploited for high-speed operation in applications that require classification of aerial targets where the SPAD imaging system and embedded device are mounted on a UAS.
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