Digital Commons Network^™

Human-Centric Smart Cities: A Digital Twin-Oriented Design Of Interactive Autonomous Vehicles, Oscar G. De Leon-Vazquez

Theses and Dissertations

Autonomous vehicle (AV) technology is introduced as a solution to improve transportation safety by eliminating traffic accidents caused by human error, which is the leading cause of 90% of accidents. One key feature of AVs is sensing and perceiving their surrounding environment through processing observations collected from the environment. The perception system is essential for an AV to make informed decisions and safely navigate the environment. This study presents an image semantic segmentation algorithm developed in the area of computer vision to improve AV perception. The U-Net-based algorithm is trained and validated using a synthetically generated dataset in a simulation …

Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken

LSU Master's Theses

Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …

Automated Approaches To Enable Innovative Civic Applications From Citizen Generated Imagery, Hye Seon Yi

USF Tampa Graduate Theses and Dissertations

Smart governance is an area, that is increasingly becoming important, not only in advanced countries, but all across the globe. Thanks to global scale network connectivity, permeance of smart-devices of various form-factors, and overall improvement in digital literary, we are now seeing "smartness" everywhere, or if not, the general public is expecting the same. Ultimately, the goal of smart governance is to facilitate state-of-the-art technologies to improve citizens’ lives. With the ubiquity of smart phone technologies today, citizens more readily participate in collaboration with public officials for improved quality of life and their communities. By utilizing optimal tools, public officials …

An Approach To Lunar Regolith Particle Detection And Classification Using Deep Learning, Hira Nadeem

Electronic Thesis and Dissertation Repository

Lunar regolith, unconsolidated rock on the lunar surface, is made up of various particles. Understanding the quantities and locations of these particles on the lunar surface is of particular interest to planetary scientists for mission planning and science objectives. There is a limited supply of lunar regolith samples available on Earth for planetary scientists to characterize. Lunar rover missions over the next decade are expected to provide high-resolution images of the lunar surface. Deep learning can be leveraged to analyze lunar regolith from image data. An object detection model using transfer learning was developed to identify and classify particles of …

Assessing High Dynamic Range Imagery Performance For Object Detection In Maritime Environments, Erasmo Landaeta

Doctoral Dissertations and Master's Theses

The field of autonomous robotics has benefited from the implementation of convolutional neural networks in vision-based situational awareness. These strategies help identify surface obstacles and nearby vessels. This study proposes the introduction of high dynamic range cameras on autonomous surface vessels because these cameras capture images at different levels of exposure revealing more detail than fixed exposure cameras. To see if this introduction will be beneficial for autonomous vessels this research will create a dataset of labeled high dynamic range images and single exposure images, then train object detection networks with these datasets to compare the performance of these networks. …

Object Detection And Classification In The Visible And Infrared Spectrums, Domenick D. Poster

Graduate Theses, Dissertations, and Problem Reports

The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark …

Tent Detection In Satellite Imagery: Responding To Natural Disasters With Unet, Zachary Roman Lazzara

Student Theses

The purpose of this research is to create a deep learning tent detection system using UNet, that can be used to guide disaster relief efforts using satellite imagery. If the tent density in a given location can be detected following a natural disaster, this may be indicative of displaced people in need of aid and can guide search and rescue teams. In this paper we produce a tent detection system utilizing UNet, which achieved an overall accuracy of 80% when compared with the ground truth, or accuracies of 86% and 67% on the training and validation subsets respectively. We also …

A Deep Learning Approach For Airport Runway Identification From Satellite Imagery, Mahmut Gemici

Theses and Dissertations

The United States lacks a comprehensive national database of private Prior Permission Required (PPR) airports. The primary reason such a database does not exist is that there are no federal regulatory obligations for these facilities to have their information re-evaluated or updated by the Federal Aviation Administration (FAA) or the local state Department of Transportation (DOT) once the data has been entered into the system. The often outdated and incorrect information about landing sites presents a serious risk factor in aviation safety. In this thesis, we present a machine learning approach for detecting airport landing sites from Google Earth satellite …

Towards Multimodal Open-World Learning In Deep Neural Networks, Manoj Acharya

Theses

Over the past decade, deep neural networks have enormously advanced machine perception, especially object classification, object detection, and multimodal scene understanding. But, a major limitation of these systems is that they assume a closed-world setting, i.e., the train and the test distribution match exactly. As a result, any input belonging to a category that the system has never seen during training will not be recognized as unknown. However, many real-world applications often need this capability. For example, self-driving cars operate in a dynamic world where the data can change over time due to changes in season, geographic location, sensor types, …

Low-Cost Uav Swarm For Real-Time Object Detection Applications, Joel Valdovinos Miranda

Master's Theses

With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an …

An Ios Application For Visually Impaired Individuals To Assist With Crossing Roads, Ali Khan

Honors Theses

In day-to-day life, visually impaired individuals face the problem of crossing roads by themselves. This project was designed and built to solve this key issue. The system is supposed to give the user a warning before approaching a crosswalk for their safety and also give information about when it is safe to cross the road. An iOS application was developed to address the problem since recent studies have discovered that a vast number of visually impaired individuals are using smartphones (iPhones in particular) due to the ease and convenience it brings to their daily life. The application should be able …

Incorporating Spatial Relationship Information In Signal-To-Text Processing, Jeremy Elon Davis

Theses and Dissertations

This dissertation outlines the development of a signal-to-text system that incorporates spatial relationship information to generate scene descriptions. Existing signal-to-text systems generate accurate descriptions in regards to information contained in an image. However, to date, no signalto- text system incorporates spatial relationship information. A survey of related work in the fields of object detection, signal-to-text, and spatial relationships in images is presented first. Three methodologies followed by evaluations were conducted in order to create the signal-to-text system: 1) generation of object localization results from a set of input images, 2) derivation of Level One Summaries from an input image, and …

Improving Intelligent Transportation Safety And Reliability Through Lowering Costs, Integrating Machine Learning, And Studying Model Sensitivity, Cavender Holt

All Theses

As intelligent transportation becomes increasingly prevalent in the domain of transportation, it is essential to understand the safety, reliability, and performance of these systems. We investigate two primary areas in the problem domain. The first area concerns increasing the feasibility and reducing the cost of deploying pedestrian detection systems to intersections in order to increase safety. By allowing pedestrian detection to be placed in intersections, the data can be better utilized to create systems to prevent accidents from occurring. By employing a dynamic compression scheme for pedestrian detection, we show the reduction of network bandwidth improved by 2.12× over the …

Parsing Structural And Textual Information From Uml Class Diagrams To Assist In Verification Of Requirement Specifications, Sandip Gautam

Culminating Projects in Computer Science and Information Technology

Unified Modeling Language (UML) class diagrams are widely used throughout software design lifecycle to model the Software Requirement Specifications in developing any software. In many cases these class diagrams are initially drawn, as well as subsequently revised using hand in a piece of paper, or a whiteboard. Although these hand-drawn class diagrams capture most of the specifications, they need a lot of revision and visual inspection by the software architects for verification of the captured requirements, of the system being modeled. Manually verifying the correctness and completeness of the class diagrams involves a lot of redundant work, and can raise …

Synthetic Augmentation Methods For Object Detection In Overhead Imagery, Nicholas R. Hamilton

Dissertations, Master's Theses and Master's Reports

The multidisciplinary area of geospatial intelligence (GEOINT) is continually changing and becoming more complex. From efforts to automate portions of GEOINT using machine learning, which augment the analyst and improve exploitation, to optimizing the growing number of sources and variables, there is no denying that the strategies involved in this collection method are rapidly progressing. The unique and inherent complexities involved in imagery analysis from an overhead perspective--—e.g., target resolution, imaging band(s), and imaging angle--—test the ability of even the most developed and novel machine learning techniques. To support advancement in the application of object detection in overhead imagery, we …

Chess Piece Detection, Craig Reid Belshe

Electrical Engineering

This project determines the positions of each piece on a physical chessboard, so that a computer can record a game of chess by noting down the piece positions at the end of each turn. It determines each move so that the game can be replayed later without watching actual footage. The project allows for easy viewing of past games. An image of the chessboard is analyzed to detect each square on the board, and each piece's location. This is then done for each turn, so that the system can keep track of an entire game.

Monocular 3d Object Detection Via Ego View-To-Bird’S Eye View Translation, Atharva Arun Tembe

Theses

The advanced development in autonomous agents like self-driving cars can be attributed to computer vision, a branch of artificial intelligence that enables software to understand the content of image and video. These autonomous agents require a three-dimensional modelling of its surrounding in order to operate reliably in the real-world. Despite the significant progress of 2D object detectors, they have a critical limitation in location sensitive applications as they do not provide accurate physical information of objects in 3D space. 3D object detection is a promising topic that can provide relevant solutions which could improve existing 2D based applications. Due to …

Methods For Detecting Floodwater On Roadways From Ground Level Images, Cem Sazara

Computational Modeling & Simulation Engineering Theses & Dissertations

Recent research and statistics show that the frequency of flooding in the world has been increasing and impacting flood-prone communities severely. This natural disaster causes significant damages to human life and properties, inundates roads, overwhelms drainage systems, and disrupts essential services and economic activities. The focus of this dissertation is to use machine learning methods to automatically detect floodwater in images from ground level in support of the frequently impacted communities. The ground level images can be retrieved from multiple sources, including the ones that are taken by mobile phone cameras as communities record the state of their flooded streets. …

A Deep Learning-Based Automatic Object Detection Method For Autonomous Driving Ships, Ojonoka Erika Atawodi

Master's Theses

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated …

Object Detection And Sensor Data Processing For Off-Road Autonomous Vehicles, Timothy Foster

Theses and Dissertations

Autonomous vehicles require intelligent systems to perceive and navigate unstructured envi- ronments. The scope of this project is to improve and develop algorithms and methods to support autonomy in the off-road problem space. This work explores computer vision architectures to support real-time object detection. Furthermore, this project explores multimodal deep fusion and sensor processing for off-road object detection. The networks are compared to and based off of the SqueezeSeg architecture. The MAVS simulator was utilized for data collection and semantic ground truth. The results indicate improvements from the SqueezeSeg performance metrics.

Supporting Space Domain Awareness Through The Development And Analysis Of Space Object Detection Algorithms Employed By Ground-Based Telescopes, Connor A. Paw

Theses and Dissertations

Detection algorithms are instrumental in maintaining space domain awareness, specifically in the observation, monitoring, and categorization of unknown space objects. State of the art detection algorithms utilize a matched filter or a spatial correlator on long exposure image data to make pixel-wise detection decisions. This thesis investigates the advantages and practical potential of two different post-processing detection algorithms that can be employed by ground-based telescopes. The first algorithm explored is based on a long exposure Fourier domain processing technique, while the second is centered around frame selection from a series of short exposure images. The results of the experiments performed …

Fire Code Violation Detection, Salim Elewa

Student Theses

his paper explores the creation of an object detection system for mobile using YOLO(You Only Look Once) algorithm., a real-time object detection model that is developed to run on a portable device such as a cellphone that does not have a Graphics Processing Unit (GPU). This algorithm is utilized to detect fire code violations, specifically the obstructed door in a fire separation: the areas surround- ing the door opening shall be kept clear of anything that would be likely to ob- struct. The machine learning algorithm utilized has been fine-tuned to fit the model based on accuracy levels. The author …

Perception System: Object And Landmark Detection For Visually Impaired Users, Chenguang Zhang

Masters Theses

This paper introduces a system which enables visually impaired users to detect objects and landmarks within the line of sight. The system works in two modes: landmark mode, which detects predefined landmarks, and object mode, which detects objects for everyday use. Users can get audio announcement for the name of the detected object or landmark as well as its estimated distances. Landmark detection helps visually impaired users explore an unfamiliar environment and build a mental map.

The proposed system utilizes a deep learning system for detection, which is deployed on the mobile phone and optimized to run in real-time. Unlike …

Improving Visual Recognition With Unlabeled Data, Aruni Roy Chowdhury

Doctoral Dissertations

The success of deep neural networks has resulted in computer vision systems that obtain high accuracy on a wide variety of tasks such as image classification, object detection, semantic segmentation, etc. However, most state-of-the-art vision systems are dependent upon large amounts of labeled training data, which is not a scalable solution in the long run. This work focuses on improving existing models for visual object recognition and detection without being dependent on such large-scale human-annotated data. We first show how large numbers of hard examples (cases where an existing model makes a mistake) can be obtained automatically from unlabeled video …

Automated Digit Recognition On Sound Pressure Level Meters Based On Deep Learning, Che-Wei Tung

Theses and Dissertations

Sound pressure level (SPL) meter is one of the useful devices used for measuring the sound level pressure. The measurement device displays the SPL value in decibels (dB) on a standard LCD screen (no backlight). We could base on the digit number shown on the LCD screen to do some adjustments or evaluations. Thus, SPL has been widely used in several fields to quantify different noise, such as industrial, environmental, and aircraft noise. However, in my basic knowledge, there is no previous study used machine learning to auto-recognize the digit on the SPL meter. This thesis presents a novel system …

Design Of Hardware Cnn Accelerators For Audio And Image Classification, Rohini Jayachandre Gillela

Theses

Ever wondered how the world was before the internet was invented? You might soon wonder how the world would survive without self-driving cars and Advanced Driver Assistance Systems (ADAS). The extensive research taking place in this rapidly evolving field is making self-driving cars futuristic and more reliable. The goal of this research is to design and develop hardware Convolutional Neural Network (CNN) accelerators for self-driving cars, that can process audio and visual sensory information. The idea is to imitate a human brain that takes audio and visual data as input while driving. To achieve a single die that can process …

Improving Face Clustering In Videos, Souyoung Jin

Doctoral Dissertations

Human faces represent not only a challenging recognition problem for computer vision, but are also an important source of information about identity, intent, and state of mind. These properties make the analysis of faces important not just as algorithmic challenges, but as a gateway to developing computer vision methods that can better follow the intent and goals of human beings. In this thesis, we are interested in face clustering in videos. Given a raw video, with no caption or annotation, we want to group all detected faces by their identity. We address three problems in the area of face clustering …

Computer Vision Gesture Recognition For Rock Paper Scissors, Nicholas Hunter

Senior Independent Study Theses

This project implements a human versus computer game of rock-paper-scissors using machine learning and computer vision. Player’s hand gestures are detected using single images with the YOLOv3 object detection system. This provides a generalized detection method which can recognize player moves without the need for a special background or lighting setup. Additionally, past moves are examined in context to predict the most probable next move of the system’s opponent. In this way, the system achieves higher win rates against human opponents than by using a purely random strategy.

Object Detection In High Resolution Aerial Images And Hyperspectral Remote Sensing Images, Yilong Liang

Theses

With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of remotely sensed images. However, the exploration of these images still involves a tremendous amount of human interventions, which are tedious, time-consuming, and inefficient. To help imaging experts gain a complete understanding of the images and locate the objects of interest in a more accurate and efficient way, there is always an urgent need for developing automatic detection algorithms. In this work, we delve into the object detection problems in remote sensing applications, exploring the detection algorithms for both hyperspectral images (HSIs) and …

On The Robustness Of Object Detection Based Deep Learning Models, Matthew Seals

Masters Theses

Object detection is one of the most popular areas in the field of computer vision and deep learning. Several advances have been reported in the literature showing promising object detection results. However, most of these results use databases of images that have been collected under almost ideal conditions and tested with input images mostly not representative of real life imagery. When tested with challenging data, most of these object detection models break down.The objective of this work is to quantify the performance of the most recent object detection models in the presence of realistic degradation in the form of differing …