SP-ILC: Concurrent Single-Pixel Imaging, Object Location, and Classification by Deep Learning

We propose a concurrent single-pixel imaging, object location, and classification scheme based on deep learning (SP-ILC). We used multitask learning, developed a new loss function, and created a dataset suitable for this project. The dataset consists of scenes that contain different numbers of possibly overlapping objects of various sizes. The results we obtained show that SP-ILC runs concurrent processes to locate objects in a scene with a high degree of precision in order to produce high quality single-pixel images of the objects, and to accurately classify objects, all with a low sampling rate. SP-ILC has potential for effective use in remote sensing, medical diagnosis and treatment, security, and autonomous vehicle control.

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Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects

Synthesis Lectures on Advances in Automotive Technology ◽

10.2200/s00932ed1v01y201906aat008 ◽

2019 ◽

Vol 3 (4) ◽

pp. 1-80 ◽

Cited By ~ 1

Author(s):

Sampo Kuutti ◽

Saber Fallah ◽

Richard Bowden ◽

Phil Barber

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Autonomous Vehicle ◽

Vehicle Control ◽

Control Algorithms ◽

Future Prospects

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Autonomous Vehicle Control for Lane and Vehicle Tracking by Using Deep Learning via Vision

2018 6th International Conference on Control Engineering & Information Technology (CEIT) ◽

10.1109/ceit.2018.8751764 ◽

2018 ◽

Author(s):

Masum Celil Olgun ◽

Zakir Baytar ◽

Kadir Metin Akpolat ◽

Ozgur Koray Sahingoz

Keyword(s):

Deep Learning ◽

Autonomous Vehicle ◽

Vehicle Control ◽

Vehicle Tracking

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A Survey of Deep Learning Applications to Autonomous Vehicle Control

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2019.2962338 ◽

2020 ◽

pp. 1-22 ◽

Cited By ~ 15

Author(s):

Sampo Kuutti ◽

Richard Bowden ◽

Yaochu Jin ◽

Phil Barber ◽

Saber Fallah

Keyword(s):

Deep Learning ◽

Autonomous Vehicle ◽

Vehicle Control

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A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

Scientific Reports ◽

10.1038/s41598-021-93227-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jared Hamwood ◽

Beat Schmutz ◽

Michael J. Collins ◽

Mark C. Allenby ◽

David Alonso-Caneiro

Keyword(s):

Deep Learning ◽

Human Performance ◽

Automatic Segmentation ◽

Ct Images ◽

Human Observer ◽

In Series ◽

Magnetic Resonance Imaging Mri ◽

High Degree ◽

Orbital Region ◽

Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

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Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) ◽

10.1109/icarcv50220.2020.9305328 ◽

2020 ◽

Author(s):

Andres E. Gomez Hernandez ◽

Ozgur Erkent ◽

Christian Laugier

Keyword(s):

Deep Learning ◽

Moving Objects ◽

Autonomous Vehicle ◽

Detector Model

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Vision-based Autonomous Vehicle Recognition

ACM Computing Surveys ◽

10.1145/3447866 ◽

2021 ◽

Vol 54 (4) ◽

pp. 1-37

Author(s):

Azzedine Boukerche ◽

Xiren Ma

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Comprehensive Evaluation ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Regional Security ◽

Future Research ◽

Learning Abilities ◽

Vehicle Recognition ◽

Model Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

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Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts

2018 IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2018.8619275 ◽

2018 ◽

Cited By ~ 6

Author(s):

Ran Tian ◽

Sisi Li ◽

Nan Li ◽

Ilya Kolmanovsky ◽

Anouck Girard ◽

...

Keyword(s):

Decision Making ◽

Autonomous Vehicle ◽

Vehicle Control ◽

Game Theoretic

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Automated Search-Based Robustness Testing for Autonomous Vehicle Software

Modelling and Simulation in Engineering ◽

10.1155/2016/5309348 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Kevin M. Betts ◽

Mikel D. Petty

Keyword(s):

Monte Carlo ◽

Performance Metrics ◽

Initial Conditions ◽

Autonomous Vehicle ◽

Vehicle Control ◽

Test Case ◽

Test Cases ◽

Control Software ◽

Robustness Testing ◽

Testing Techniques

Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.

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