scholarly journals Semantic segmentation with deep learning: detection of cracks at the cut edge of glass

2020 ◽  
Author(s):  
Michael Drass ◽  
Hagen Berthold ◽  
Michael A. Kraus ◽  
Steffen Müller-Braun
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Classical Problem ◽  
Semantic Segmentation ◽  
Neural Net ◽  
Cut Edge ◽  
Glass Processing ◽  
Time Savings ◽  
First Time ◽  
Glass Edge

Abstract In this paper, artificial intelligence (AI) will be applied for the first time in the context of glass processing. The goal is to use an algorithm based on artificial intelligence to detect the fractured edge of a cut glass in order to generate a so-called mask image by AI. In the context of AI, this is a classical problem of semantic segmentation, in which objects (here the cut-edge of the cut glass) are automatically surrounded by the power of AI or detected and drawn. An original image of a cut glass edge is implemented into a deep neural net and processed in such a way that a mask image, i.e. an image of the cut edge, is automatically generated. Currently, this is only possible by manual tracing the cut-edge due to the fact that the crack contour of glass can sometimes only be recognized roughly. After manually marking the crack using an image processing program, the contour is then automatically evaluated further. AI and deep learning may provide the potential to automate the step of manual detection of the cut-edge of cut glass to great extent. In addition to the enormous time savings, the objectivity and reproducibility of detection is an important aspect, which will be addressed in this paper.

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.

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.

Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats

2021 ◽  
pp. 019262332098324 ◽  
Author(s):  
Hannah Pischon ◽  
David Mason ◽  
Bettina Lawrenz ◽  
Olivier Blanck ◽  
Anna-Lena Frisk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Direct Detection ◽  
Digital Pathology ◽  
Specific Cell ◽  
Cytoplasmic Area ◽  
Quantitative Results ◽  
Hematoxylin And Eosin ◽  
Accuracy And Repeatability ◽  
First Time

Digital pathology evolved rapidly, enabling more systematic usage of image analysis and development of artificial intelligence (AI) applications. Here, combined AI models were developed to evaluate hepatocellular hypertrophy in rat liver, using commercial AI-based software on hematoxylin and eosin-stained whole slide images. In a first approach, deep learning-based identification of critical tissue zones (centrilobular, midzonal, and periportal) enabled evaluation of region-specific cell size. Mean cytoplasmic area of hepatocytes was calculated via several sequential algorithms including segmentation in microanatomical structures (separation of sinusoids and vessels from hepatocytes), nuclear detection, and area measurements. An increase in mean cytoplasmic area could be shown in groups given phenobarbital, known to induce hepatocellular hypertrophy when compared to control groups, in multiple studies. Quantitative results correlated with the gold standard: observation and grading performed by board-certified veterinary pathologists, liver weights, and gene expression. Furthermore, as a second approach, we introduce for the first time deep learning-based direct detection of hepatocellular hypertrophy with similar results. Cell hypertrophy is challenging to pick up, particularly in milder cases. Additional evaluation of mean cytoplasmic area or direct detection of hypertrophy, combined with histopathological observations and liver weights, is expected to increase accuracy and repeatability of diagnoses and grading by pathologists.

scholarly journals Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 327
Author(s):  
Ramiz Yilmazer ◽  
Derya Birant
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Experimental Studies ◽  
Grocery Stores ◽  
Software Application ◽  
Explainable Artificial Intelligence ◽  
Key Factor ◽  
First Time

Providing high on-shelf availability (OSA) is a key factor to increase profits in grocery stores. Recently, there has been growing interest in computer vision approaches to monitor OSA. However, the largest and well-known computer vision datasets do not provide annotation for store products, and therefore, a huge effort is needed to manually label products on images. To tackle the annotation problem, this paper proposes a new method that combines two concepts “semi-supervised learning” and “on-shelf availability” (SOSA) for the first time. Moreover, it is the first time that “You Only Look Once” (YOLOv4) deep learning architecture is used to monitor OSA. Furthermore, this paper provides the first demonstration of explainable artificial intelligence (XAI) on OSA. It presents a new software application, called SOSA XAI, with its capabilities and advantages. In the experimental studies, the effectiveness of the proposed SOSA method was verified on image datasets, with different ratios of labeled samples varying from 20% to 80%. The experimental results show that the proposed approach outperforms the existing approaches (RetinaNet and YOLOv3) in terms of accuracy.

scholarly journals LSTM Accelerator for Convolutional Object Identification

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 157 ◽  
Author(s):  
Alkiviadis Savvopoulos ◽  
Andreas Kanavos ◽  
Phivos Mylonas ◽  
Spyros Sioutas
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
State Of The Art ◽  
Object Identification ◽  
Image Sequences ◽  
Neural Net ◽  
Trade Off ◽  
Convolutional Networks ◽  
Average Improvement ◽  
Increase In Accuracy

Deep Learning has dramatically advanced the state of the art in vision, speech and many other areas. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this paper, in order to detect the version that can provide the best trade-off in terms of time and accuracy, convolutional networks of various depths have been implemented. Batch normalization is also considered since it acts as a regularizer and achieves the same accuracy with fewer training steps. For maximizing the yield of the complexity by diminishing, as well as minimizing the loss of accuracy, LSTM neural net layers are utilized in the process. The image sequences are proven to be classified by the LSTM in a more accelerated manner, while managing better precision. Concretely, the more complex the CNN, the higher the percentages of exactitude; in addition, but for the high-rank increase in accuracy, the time was significantly decreased, which eventually rendered the trade-off optimal. The average improvement of performance for all models regarding both datasets used amounted to 42 % .

scholarly journals Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

2020 ◽  
Vol 5 (3) ◽  
pp. 247-285
Author(s):  
M. A. Kraus ◽  
M. Drass
Keyword(s):  
Artificial Intelligence ◽  
Structural Engineering ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Current Status ◽  
Future Research ◽  
Structural Glass ◽  
Material Parameter Identification ◽  
Cut Edge ◽  
Potential Applications

Abstract’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.

scholarly journals Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

2020 ◽  
Vol 9 (3) ◽  
pp. 871 ◽  
Author(s):  
Muhammad Arsalan ◽  
Muhammad Owais ◽  
Tahir Mahmood ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Disease Diagnosis ◽  
Montgomery County ◽  
Single Class ◽  
Lung Segmentation ◽  
X Ray ◽  
Clinical Criteria ◽  
Cardiothoracic Ratio

Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

scholarly journals Deep Learning based Brain Tumour Segmentation

2021 ◽  
Vol 19 ◽  
pp. 234-241
Author(s):  
Pattabiraman V. ◽  
Harshit Singh
Keyword(s):  
Artificial Intelligence ◽  
Image Segmentation ◽  
Deep Learning ◽  
Loss Function ◽  
Brain Mri ◽  
Semantic Segmentation ◽  
Tumour Segmentation ◽  
Segmentation Image ◽  
High Degree ◽  
Deep Learning Model

Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN based Semantic Segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architecture and experiment with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss function like dice loss function, hierarchical dice loss function cross entropy, etc.

scholarly journals A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2675
Author(s):  
Zewei Wang ◽  
Change Zheng ◽  
Jiyan Yin ◽  
Ye Tian ◽  
Wenbin Cui
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Forest Fire ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Smoke Detection ◽  
Fire Smoke ◽  
First Time ◽  
Deep Learning Model ◽  
Selection Of

Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.

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