scholarly journals Learning to See the Hidden Part of the Vehicle in the Autopilot Scene

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 331 ◽  
Author(s):  
Yifeng Xu ◽  
Huigang Wang ◽  
Xing Liu ◽  
Henry He ◽  
Qingyue Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Object Detection ◽  
Vision System ◽  
Image Inpainting ◽  
Semantic Segmentation ◽  
Fatal Crash ◽  
White Side ◽  
Original Size ◽  
High Level

Recent advances in deep learning have shown exciting promise in low-level artificial intelligence tasks such as image classification, speech recognition, object detection, and semantic segmentation, etc. Artificial intelligence has made an important contribution to autopilot, which is a complex high-level intelligence task. However, the real autopilot scene is quite complicated. The first accident of autopilot occurred in 2016. It resulted in a fatal crash where the white side of a vehicle appeared similar to a brightly lit sky. The root of the problem is that the autopilot vision system cannot identify the part of a vehicle when the part is similar to the background. A method called DIDA was first proposed based on the deep learning network to see the hidden part. DIDA cascades the following steps: object detection, scaling, image inpainting assuming a hidden part beside the car, object re-detection from inpainted image, zooming back to the original size, and setting an alarm region by comparing two detected regions. DIDA was tested in a similar scene and achieved exciting results. This method solves the aforementioned problem only by using optical signals. Additionally, the vehicle dataset captured in Xi’an, China can be used in subsequent research.

scholarly journals Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

2021 ◽  
pp. 1748006X2110210
Author(s):  
Andreas Brandsæter ◽  
Ottar L Osen
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Decision Support ◽  
Object Detection ◽  
Autonomous Navigation ◽  
Adversarial Networks ◽  
Maritime Navigation ◽  
Ship Navigation ◽  
Autonomous Ship ◽  
Ship Operation

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.

scholarly journals A Survey of Graphical Page Object Detection with Deep Neural Networks

2021 ◽  
Author(s):  
Jwalin Bhatt ◽  
Khurram Azeem Hashmi ◽  
Muhammad Zeshan Afzal ◽  
Didier Stricker
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Conceptual Understanding ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Approaches ◽  
Document Images ◽  
Essential Information ◽  
Current State ◽  
High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

Deep Learning Network

2020 ◽  
pp. 1-30
Author(s):  
Bhanu Chander
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Great Power ◽  
Human Being ◽  
Technology Improvement ◽  
Deep Learning Network ◽  
High Level ◽  
Human Brains ◽  
Near Future

Artificial intelligence (AI) is defined as a machine that can do everything a human being can do and produce better results. Means AI enlightening that data can produce a solution for its own results. Inside the AI ellipsoidal, Machine learning (ML) has a wide variety of algorithms produce more accurate results. As a result of technology, improvement increasing amounts of data are available. But with ML and AI, it is very difficult to extract such high-level, abstract features from raw data, moreover hard to know what feature should be extracted. Finally, we now have deep learning; these algorithms are modeled based on how human brains process the data. Deep learning is a particular kind of machine learning that provides flexibility and great power, with its attempts to learn in multiple levels of representation with the operations of multiple layers. Deep learning brief overview, platforms, Models, Autoencoders, CNN, RNN, and Appliances are described appropriately. Deep learning will have many more successes in the near future because it requires very little engineering by hand.

scholarly journals Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation

2020 ◽  
Vol 10 (20) ◽  
pp. 7347
Author(s):  
Jihyo Seo ◽  
Hyejin Park ◽  
Seungyeon Choo
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Problem Solving ◽  
Architectural Design ◽  
Semantic Segmentation ◽  
Data Sets ◽  
Functional Requirements ◽  
Architectural Drawings ◽  
Design Alternatives ◽  
Automation Technology

Artificial intelligence presents an optimized alternative by performing problem-solving knowledge and problem-solving processes under specific conditions. This makes it possible to creatively examine various design alternatives under conditions that satisfy the functional requirements of the building. In this study, in order to develop architectural design automation technology using artificial intelligence, the characteristics of an architectural drawings, that is, the architectural elements and the composition of spaces expressed in the drawings, were learned, recognized, and inferred through deep learning. The biggest problem in applying deep learning in the field of architectural design is that the amount of publicly disclosed data is absolutely insufficient and that the publicly disclosed data also haves a wide variety of forms. Using the technology proposed in this study, it is possible to quickly and easily create labeling images of drawings, so it is expected that a large amount of data sets that can be used for deep learning for the automatic recommendation of architectural design or automatic 3D modeling can be obtained. This will be the basis for architectural design technology using artificial intelligence in the future, as it can propose an architectural plan that meets specific circumstances or requirements.

scholarly journals Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

2019 ◽  
Vol 8 (9) ◽  
pp. 1446 ◽  
Author(s):  
Arsalan ◽  
Owais ◽  
Mahmood ◽  
Cho ◽  
Park
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Medical Image Analysis ◽  
Semantic Segmentation ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Images ◽  
Computer Assisted ◽  
Hypertensive Retinopathy ◽  
Retinal Vessels

Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

scholarly journals Video Object Detection through Traditional and Deep Learning Methods

2020 ◽  
Vol 9 (4) ◽  
pp. 1822-1826
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Sparse Matrix ◽  
Image Understanding ◽  
Low Rank ◽  
Detection Methods ◽  
Video Object ◽  
Learning Methods ◽  
Primary Focus ◽  
High Level

Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem solving in object detection algorithms. We have discussed various object detection methods and challenges in this paper.

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