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Traffic Signs Recognition in a mobile-based application using TensorFlow and Transfer Learning technics 15

Traffic Signs Recognition in a mobile-based application usin...

引用

ieeE 15th International Conference of System of Systems Engineering (SoSE)

作者： Benhamida, Abdallah Varkonyi-Koczy, Annamaria R. Kozlovszky, Miklos Obuda Univ Doctoral Sch Appl Informat & Appl Math BioTech Res Ctr EKIK Budapest Hungary J Selye Univ Dept Math & Informat Komarno Slovakia Obuda Univ Inst Automat Budapest Hungary Obuda Univ Inst Biomat John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9781728180502

Nowadays, Machine Learning applications are spreading widely in different science and research fields which gave, in fact, the possibility to enhance the results of all kind of both, automated tasks and further possible application areas. Autonomous smart driving cars presents one of the major fields that uses machine learning technics to push further the automated tasks inside the car systems. Many types of research related to this topic enabled real application to fully automate some parts of the car driving process. Road lane detection, pedestrian and car approximation detection, and fastest road finding using real-time traffic statistics present some of the possible application areas that could use the Machine learning technics to improve autonomous driving cars systems. Traffic signs present an important part of the daily driving routine, therefore, traffic signs recognition for mobile-based application is a great solution that provides a new layer for autonomous car driving systems. In this paper, we propose a powerful tool for traffic signs recognition in a mobile-based application. This tool uses TensorFlow together with transfer learning technic that makes it easier to train our dataset on a pre-trained Model using the convolutional network (ConvNet). The used model is a Single Shot MultiBox Detector (SSD) MobileNet V2 based model which uses one single deep network to train the model on multiple objects per image. This network uses 300x300 annotated input images with multiple objects to provide faster training time and faster detection results compared to other types of neural networks. The annotation is made by providing the coordinates of the rectangle that surrounds the given object together with its label which defines the name of the object. The coordinates are usually given by providing the (x,y) coordinates of the top-left and bottom-right points of the surrounding rectangle. This presents a powerful technic for real-time detection on mobile devices

关键词： machine learning traffic signs object detection TensorFlow SSD Convolutional network image processing

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A Deep Learning Approach for on-site Plant Leaf Detection 14

A Deep Learning Approach for on-site Plant Leaf Detection

引用

ieeE 14th International colloquium on Signal processing and its applications (CSPA)

作者： Cap, Huu Quan Suwa, Katsumasa Fujita, Erika Kagiwada, Satoshi Uga, Hiroyuki Iyatomi, Hitoshi Hosei Univ Grad Sch Sci & Engn Appl Informat Tokyo Japan Hosei Univ Fac Biosci & Appl Chem Clin Plant Sci Tokyo Japan Saitama Agr Technol Res Ctr Saitama Japan

ISBN: (纸本)9781538603895

Plant diseases are the major problem in the worldwide agriculture sector. Therefore, the early detection is essential for reducing economic losses and mitigating the seriousness of the global food problem. Some fast and accurate computer-based methods have been applied to detect plant diseases. However, as far as our best knowledge, all those methodologies only accept a narrow range image, typically one or limited number of target(s) are in the image frame as their input. Thus, they are time-consuming and difficult to be applied for onsite wide range images (e.g. images or videos from stationary surveillance camera). In this paper, we propose leaf localization method from on-site wide-angle images with a deep learning approach. Our method achieves a detection performance of 78.0% in F1-measure at 2.0 fps.

关键词： deep learning plant disease plant diagnosis convolutional neural networks object detection

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Traffic Sign Integrity Analysis Using Deep Learning 14

Traffic Sign Integrity Analysis Using Deep Learning

引用

ieeE 14th International colloquium on Signal processing and its applications (CSPA)

作者： Acilo, Joshua Paolo N. Dela Cruz, Allyana Grace S. Kaw, Michael Kevin L. Mabanta, Maynald D. Pineda, Vemeni Grace G. Roxas, Edison A. Univ Santo Tomas Espana Dept Elect Engn Fac Engn Manila Philippines

ISBN: (纸本)9781538603895

Standardization and harmonization of high-quantity and low-cost roadway assets such as traffic signs make a compelling case now, especially in the matter of road safety and the advent of autonomous transportation vehicles. As for the standardization and degradation problem in classifying traffic signs, no automatic approach has been developed yet. This paper presents two trained models that can classify the compliance of a traffic sign to a prescribed design standard in accordance to the Vienna Convention on Road Signs and Signals adapted by the Philippines, and its physical degradation status. Due to the limited size of image dataset and a weak computing resource, a deep learning approach using transfer learning was utilized. The process highlights the use of a state-of-the-art pre-trained convolutional neural network - the ResNet-50 which was trained on a subset of the imageNet database. This network was then fine-tuned on the curated dataset of traffic signs derived on Google Street View with varying design and physical condition. The experiments conducted exhibit test accuracy of 95.70% in the compliance model and 96.20% in the degradation model which shows a great potential in developing a streamlined traffic sign assessment platform.

关键词： Traffic Signs Google Street View Deep Learning Convolutional neural networks Transfer Learning ResNet-50

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Improved optical character recognition with deep neural network

Improved optical character recognition with deep neural netw...

引用

International colloquium on Signal processing & Its applications, CSPA

作者： Tan Chiang Wei U. U. Sheikh Ab Al-Hadi Ab Rahman Intel Microelectronics (M) Sdn. Bhd. Pulau Pinang Malaysia Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia Universiti Teknologi Malaysia Skudai Johor MY

Optical Character Recognition (OCR) plays an important role in the retrieval of information from pixel-based images to searchable and machine-editable text formats. In old or poorly printed documents, printed characters are typically broken and blurred, making character recognition potentially far more complex. In this work, deep neural network using Inception V3 is used to train and perform OCR. The Inception V3 network is trained with 53,342 noisy character images, which were collected from receipts and newspapers. Our experimental results show that the proposed deep neural network achieved significantly better recognition accuracy on poor quality text images and resulted in an overall 21.5% reduction in error rate compared to existing OCRs.

关键词： Optical character recognition software Training Character recognition image segmentation neural networks Optical imaging Optical signal processing

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Traffic sign integrity analysis using deep learning

Traffic sign integrity analysis using deep learning

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International colloquium on Signal processing & Its applications, CSPA

作者： Joshua Paolo N. Acilo Allyana Grace S. Dela Cruz Michael Kevin L. Kaw Maynald D. Mabanta Vemeni Grace G. Pineda Edison A. Roxas Department of Electronics Engineering University of Santo Tomas España Manila Philippines

关键词： Training Machine learning Degradation Roads Google Convolutional neural networks Manuals

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Analyzing selected visual anomaly through ST-based multi-resolution VEP decomposition

Analyzing selected visual anomaly through ST-based multi-res...

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International colloquium on Signal processing & Its applications, CSPA

作者： Vikneswaran Vijean M. Hariharan M.N. Mansor School of Mechatronic Engineering University Malaysia Perlis Perlis Malaysia

ISBN: (纸本)9781467387811

Visual anomaly interferes with the normal sight, and therefore can cause discomfort. Ophthalmologist describes a vision anomaly as any kind of vision loss, be it partial or total vision loss. The cause of vision loss can be traced back to genetic conditions, improper lifestyle, medical conditions and accidental causes. Visually evoked potentials (VEP) are often used in cases where the medical experts might have difficulties in determining the cause of the problem. VEP has often been reported in the literature as a reliable method for detecting visual abnormalities. By analyzing the recorded brain responses toward a visual stimulus, it is possible for the ophthalmologist to make an appropriate estimation. Since the analysis of VEP by the medical experts is still subjective, there is a need for a more comprehensive analysis method. Researchers are now trying to explore the spectral information of the VEP for easier diagnosis. In this regard, this work explores the use of Stockwell transform (ST) for the analysis of VEP signals. Via ST-based multi-resolution decomposition of the signals, the time-frequency information matrix (ST-matrix) is obtained. Statistical features extracted from ST-matrix were classified using least square support vector machine (LSSVM) and probabilistic neural network (PNN). The investigation results suggest that the LSSVM classifier was able to provide 96.07% of accurate prediction for selected visual anomaly investigated.

关键词： Visualization image color analysis Transforms Support vector machines Feature extraction Biological neural networks Electrodes

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Handwriting recognition using webcam for data entry

Handwriting recognition using webcam for data entry

引用

International colloquium on Signal processing & Its applications, CSPA

作者： Wong Yoong Xiang Patrick Sebastian Electrical and Electronics Engineering Department Universiti Teknologi PETRONAS Tronoh Perak Darul Ridzuan Malaysia

This paper presents the development of a system that is robust enough to recognize numerical handwritings with the lowest error. The first test was done with a neural network trained with only the Character Vector Module as its feature extraction method. A result that is far below the set point of the recognition accuracy was achieved, a mere average of 64.67% accuracy. However, the testing were later enhanced with another feature extraction module, which consists of the combination of Character Vector Module, Kirsch Edge Detection Module, Alphabet Profile Feature Extraction Module, Modified Character Module and image Compression Module. The modules have its distinct characteristics which is trained using the Back-Propagation algorithm to cluster the pattern recognition capabilities among different samples of handwriting. Several untrained samples of numerical handwritten data were obtained at random from various people to be tested with the program. The second tests shows far greater results compared to the first test, have yielded an average of 84.52% accuracy. Further feature extraction modules are being recommended and an additional feature extraction module was added for the third test, which successfully yields 90.67%.

关键词： Feature extraction Accuracy Handwriting recognition Training neural networks Neurons image edge detection

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Content based image retrieval through feed forward neural networks

Content based image retrieval through feed forward neural ne...

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2012 ieeE 8th International colloquium on Signal processing and Its applications, CSPA 2012

作者： Rao, T.K. Rama Krishna Rani, R Usha Department of Information Technology Aditya Institute of Technology and Management Tekkali India Department of Computer Science Krishna University Machilipatnam India

ISBN: (纸本)9781467309615

We present you a process-method that has no previous knowledge of image appearance sample within the database, but retrieves basing on content information of image called content based image retrieval. Here we are trying to enhance the work nature of image retrieving with more efficient accuracy in image similarity through multilayer feed forward network perceptron named Radial basis Function neural networks such that the input parameters given will able to provide us with most accurate relevant image to that of given query image, promoting through applying k means clustering to form clusters which are parameterized with Gaussian function application. Finally compare the actual output with observed output, the weighted neurons adjustment for getting the actual accurate output. © 2012 ieeE.

关键词： image enhancement

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Model-based gait analysis for gender recognition

Model-based gait analysis for gender recognition

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2012 ieeE 8th International colloquium on Signal processing and Its applications, CSPA 2012

作者： Ismail, Ahmad Puad Tahir, Nooritawati Md Hussain, Aini Faculty of Electrical Engineering Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia

ISBN: (纸本)9781467309615

Gender classification via model-based human gait data is still immature. Hence in this research, the possibility of side view human gait silhouette to be used as gender recognition is evaluated using model-based approach. Firstly, six attributes located at lower part of human gait specifically from below waist onwards have been identified as the significant points are skeletonized based on the human gait silhouette attained. Next the vertical angles of both hip and knee with respect to thigh for 32 image sequences are extracted as feature vectors followed by feature selection via statistical analysis specifically analysis of variance along with multiple comparison procedure. Further, the resultant of feature selection acted as inputs to the artificial neural network classifier. Initial findings with accuracy of 90% and above confirmed that the proposed method suited to be utilized as gender recognition based on human gait. © 2012 ieeE.

关键词： neural networks

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Intelligent brain hemorrhage diagnosis using artificial neural networks

Intelligent brain hemorrhage diagnosis using artificial neur...

引用

2012 ieeE Business, Engineering and Industrialapplications colloquium, BEIAC 2012

作者： Balasooriya, Ushani Perera, M.U.S. Colombo Sri Lanka

ISBN: (纸本)9781467304269

Brain hemorrhage is a type of stroke which is caused by an artery in the brain bursting and causing bleeding in the surrounded tissues. Diagnosing brain hemorrhage, which is mainly through the examination of a CT scan enables the accurate prediction of disease and the extraction of reliable and robust measurement for patients in order to describe the morphological changes in the brain as the recovery progresses. Though a lot of research on medical image processing has been done, still there is opportunity for further research in the area of brain hemorrhage diagnosis due to the low accuracy level in the current methods and algorithms, coding complexity of the developed approaches, impracticability in the real environment, and lack of other enhancements which may make the system more interactive and useful. Additionally many of the existing approaches address the diagnosis of a limited no of brain hemorrhage types. This project investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages, can be used to verify expert diagnosis and also as a learning tool for trainee radiologists to minimize errors in current methods. The prototype developed using Matlab can help medical students to practice the related concepts they learn using an image guide with examples for surgeries and surgical simulation. System was evaluated by the domain experts, like radiologists, intended users such as medical students as well as by technical experts. The prototype developed was successful since it was being evaluated as credible, innovative and useful software for the students in the field of radiology while 100% of the evaluators mentioned the diagnosis accuracy is acceptable. © 2012 ieeE.

关键词： Diagnosis

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