Advanced photon counting techniques for long-range depth imaging

The Time-Correlated Single-Photon Counting (TCSPC) technique has emerged as a candidate approach for Light Detection and Ranging (LiDAR) and active depth imaging applications. The work of this Thesis concentrates on the development and investigation of functional TCSPC-based long-range scanning time-of-flight (TOF) depth imaging systems. Although these systems have several different configurations and functions, all can facilitate depth profiling of remote targets at low light levels and with good surface-to-surface depth resolution. Firstly, a Superconducting Nanowire Single-Photon Detector (SNSPD) and an InGaAs/InP Single-Photon Avalanche Diode (SPAD) module were employed for developing kilometre-range TOF depth imaging systems at wavelengths of ~1550 nm. Secondly, a TOF depth imaging system at a wavelength of 817 nm that incorporated a Complementary Metal-Oxide-Semiconductor (CMOS) 32×32 Si-SPAD detector array was developed. This system was used with structured illumination to examine the potential for covert, eye-safe and high-speed depth imaging. In order to improve the light coupling efficiency onto the detectors, the arrayed CMOS Si-SPAD detector chips were integrated with microlens arrays using flip-chip bonding technology. This approach led to the improvement in the fill factor by up to a factor of 15. Thirdly, a multispectral TCSPC-based full-waveform LiDAR system was developed using a tunable broadband pulsed supercontinuum laser source which can provide simultaneous multispectral illumination, at wavelengths of 531, 570, 670 and ~780 nm. The investigated multispectral reflectance data on a tree was used to provide the determination of physiological parameters as a function of the tree depth profile relating to biomass and foliage photosynthetic efficiency. Fourthly, depth images were estimated using spatial correlation techniques in order to reduce the aggregate number of photon required for depth reconstruction with low error. A depth imaging system was characterised and re-configured to reduce the effects of scintillation due to atmospheric turbulence. In addition, depth images were analysed in terms of spatial and depth resolution

CMOS Sensors for Time-Resolved Active Imaging

In the past decades, time-resolved imaging such as fluorescence lifetime or time-of-flight depth imaging has been extensively explored in biomedical and industrial fields because of its non-invasive characterization of material properties and remote sensing capability. Many studies have shown its potential and effectiveness in applications such as cancer detection and tissue diagnoses from fluorescence lifetime imaging, and gesture/motion sensing and geometry sensing from time-of-flight imaging. Nonetheless, time-resolved imaging has not been widely adopted due to the high cost of the system and performance limits. The research presented in this thesis focuses on the implementation of low-cost real-time time-resolved imaging systems. Two image sensing schemes are proposed and implemented to address the major limitations. First, we propose a single-shot fluorescence lifetime image sensors for high speed and high accuracy imaging. To achieve high accuracy, previous approaches repeat the measurement for multiple sampling, resulting in long measurement time. On the other hand, the proposed method achieves both high speed and accuracy at the same time by employing a pixel-level processor that takes and compresses the multiple samples within a single measurement time. The pixels in the sensor take multiple samples from the fluorescent optical signal in sub-nanosecond resolution and compute the average photon arrival time of the optical signal. Thanks to the multiple sampling of the signal, the measurement is insensitive to the shape or the pulse-width of excitation, providing better accuracy and pixel uniformity than conventional rapid lifetime determination (RLD) methods. The proposed single-shot image sensor also improves the imaging speed by orders of magnitude compared to other conventional center-of-mass methods (CMM). Second, we propose a 3-D camera with a background light suppression scheme which is adaptable to various lighting conditions. Previous 3-D cameras are not operable in outdoor conditions because they suffer from measurement errors and saturation problems under high background light illumination. We propose a reconfigurable architecture with column-parallel discrete-time background light cancellation circuit. Implementing the processor at the column level allows an order of magnitude reduction in pixel size as compared to existing pixel-level processors. The column-level approach also provides reconfigurable operation modes for optimal performance in all lighting conditions. For example, the sensor can operate at the best frame-rate and resolution without the presence of background light. If the background light saturates the sensor or increases the shot noise, the sensor can adjust the resolution and frame-rate by pixel binning and superresolution techniques. This effectively enhances the well capacity of the pixel to compensate for the increase shot noise, and speeds up the frame processing to handle the excessive background light. A fabricated prototype sensor can suppress the background light more than 100-klx while achieving a very small pixel size of 5.9μm.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136950/1/eecho_1.pd

System for measuring steel scrap volume using depth imaging

Abstract. Sustainability and green values are major themes in the world today. Companies across all fields are constantly implementing new technologies to reduce emissions and to limit the magnitude of global warming. The steel industry in general is one of the major producers of carbon dioxide emissions. The objective of this thesis was to develop a system to measure the volume of scrap metal being charged to an electric arc furnace. Obtaining the scrap volume would help the furnace operators in timing the charging of scrap baskets, thus avoiding the adverse effects resulting from early and late charging. The intention is to increase the energy efficiency of the process. The theory section of the thesis provides a short overview of the electric arc furnace process and a more detailed description of the charging process. Depth imaging technologies are then explored from a theoretical standpoint to provide the background for the selection and usage of imaging hardware. In this thesis, design science research methodology was utilized to develop the scrap volume measurement system, which consists of imaging hardware and developed software. The actual contribution of this thesis is the algorithm to extract the height of the scrap surface level from a 3-dimensional image of scrap baskets. The development process was iteratively carried out in a steel factory. The system performance was evaluated in a real-world scenario. It was established that the system was able to capture 3-dimensional data from scrap baskets and determine the scrap surface level height according to the algorithm. However, for some cases the image capturing did not perform as expected. These failure cases were a result of either steel dust obstructing the scene or the inability of the camera to capture data from unreflective material. Further research prospects were identified during conducting of the thesis. The failure cases could be addressed either programmatically, with new hardware technology, or a combination of both. Also, research could be conducted on the usage of the information provided by the system in actual charging events with the goal of optimizing charging timing

Analysis, Modeling and Dynamic Optimization of 3D Time-of-Flight Imaging Systems

The present thesis is concerned with the optimization of 3D Time-of-Flight (ToF) imaging systems. These novel cameras determine range images by actively illuminating a scene and measuring the time until the backscattered light is detected. Depth maps are constructed from multiple raw images. Usually two of such raw images are acquired simultaneously using special correlating sensors. This thesis covers four main contributions: A physical sensor model is presented which enables the analysis and optimization of the process of raw image acquisition. This model supports the proposal of a new ToF sensor design which employs a logarithmic photo response. Due to asymmetries of the two read-out paths current systems need to acquire the raw images in multiple instances. This allows the correction of systematic errors. The present thesis proposes a method for dynamic calibration and compensation of these asymmetries. It facilitates the computation of two depth maps from a single set of raw images and thus increases the frame rate by a factor of two. Since not all required raw images are captured simultaneously motion artifacts can occur. The present thesis proposes a robust method for detection and correction of such artifacts. All proposed algorithms have a computational complexity which allowsreal-time execution even on systems with limited resources (e.g. embeddedsystems). The algorithms are demonstrated by use of a commercial ToF camera

Evaluation of swine gestation-farrowing facility space and management for improving production, welfare, and infectious disease containment

The United States (US) swine industry plays an important role in providing a safe and reliable source of animal proteins for a growing world population. As the industry evolves and society advances, producers face new and complex challenges such as optimizing animal production, welfare, and health. This dissertation contributes novel evidence-based knowledge to address current swine housing and management challenges in several key areas that formed the objectives of this dissertation, which were to: develop a computer vision system to monitor sow behavior in farrowing stalls (Chapter 2), evaluate the impacts of farrowing stall layout and number of heat lamps on sow and piglet productivity (Chapter 3) and behavior (Chapter 4), quantify the static and dynamic space usage of late gestation sows (Chapter 5), and determine supplemental heat requirements to implement ventilation shut down plus and virus inactivation (Chapter 6). The research presented in this dissertation contains the following discoveries. In Chapter 2, a large-scale computer vision system was established and implemented to simultaneously and continually monitor 60 farrowing stalls. The semi-automatic image processing algorithm achieved sow posture classification accuracies of \u3e99.2% (sitting: 99.4%, standing: 99.2%, kneeling: 99.7%, lying: 99.9%) and \u3e97% accuracy for sow behaviors (feeding: 97.0%, drinking: 96.8%, other: 95.5%). The computer vision system provided the foundation for carrying out the subsequent study concerning the impact of farrowing stall layout and management strategies. It was revealed in Chapter 3 that farrowing stall physical dimensions and number of heat lamps for localized heating did not significantly impact the percentage of pre-weaning mortality, overlay, number of piglets born alive, number weaned, average daily weight gain, or litter uniformity. Stall layout did significantly influence percent stillborn; however, the difference was not of practical significance. While experimental treatment did not significantly impact production outcomes, there were significant sow and piglet behavioral differences which are reported in Chapter 4. It was found that sows in wider stalls spend more time lying down and less time sitting. Piglets in stall layouts with expanded creep areas spent more time in the creep and less time near the sow compared to traditional stall layouts. Further, when two heat lamps were used sows spent significantly more time lying and piglets spent a greater proportion of time in the heated areas. Static and dynamic space usage of individually housed gestating sows was quantified and reported in Chapter 5. An average 228 kg sow requires stall dimensions of 196 × 115 × 93 cm (L × W × H) to provide uninhibited space. To accommodate average to 95th percentile (267 kg) sows, minimum stall dimensions need to be 204 × 112 × 95 cm. The 95th percentile sow space usage had a 4% decrease in length, 84% increase in width, and 5% decrease in height compared to typical gestation stall dimensions. Chapter 6 describes the development of a model to predict minimum supplemental heat requirements for ventilation shut down plus and virus inactivation (VSD+). Tables are presented with heating values needed to achieve greater than 95% mortality within 1 h of VSD onset, as well as for virus inactivation for African Swine Fever (ASF). Requirements of supplemental heat for various pig body weights, ambient conditions, facility air tightness, and stages of production are estimated. Overall, this dissertation provides information to fill knowledge gaps regarding current challenges in the US swine industry. Results can be used to guide producers as they strive to provide safe and reliable pork for the growing world population while safeguarding wellbeing of the animals