scholarly journals An Acceleration Method based on Deep Learning and Multilinear Feature Space

2021 ◽  
Vol 12 (5) ◽  
pp. 9-26
Author(s):  
Michel Andre L .Vinagreiro ◽  
Edson C. Kitani ◽  
Armando Antonio M. Lagana ◽  
Leopoldo R. Yoshioka
Keyword(s):  
Computer Vision ◽  
Transfer Learning ◽  
Feature Space ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Traffic Sign ◽  
Real Time System ◽  
Sign Recognition ◽  
Fast Classification ◽  
Classification Time

Computer vision plays a crucial role in Advanced Assistance Systems. Most computer vision systems are based on Deep Convolutional Neural Networks (deep CNN) architectures. However, the high computational resource to run a CNN algorithm is demanding. Therefore, the methods to speed up computation have become a relevant research issue. Even though several works on architecture reduction found in the literaturehave not yet been achievedsatisfactory results for embedded real-time system applications. This paper presents an alternative approach based on the Multilinear Feature Space (MFS) method resorting to transfer learning from large CNN architectures. The proposed method uses CNNs to generate feature maps, although it does not work as complexity reduction approach. After the training process, the generated features maps are used to create vector feature space. We use this new vector space to make projections of any new sample to classify them. Our method, named AMFC, uses the transfer learning from pre-trained CNN to reduce the classification time of new sample image, with minimal accuracy loss. Our method uses the VGG-16 model as the base CNN architecture for experiments; however, the method works with any similar CNN model. Using the well-known Vehicle Image Database and the German Traffic Sign Recognition Benchmark, we compared the classification time of the original VGG-16 model with the AMFCmethod, and our method is, on average, 17 times faster. The fast classification time reduces the computational and memory demands in embedded applications requiring a large CNN architecture.

scholarly journals Using Multilinear Feature Space to Accelerate CNN Classification

2021 ◽  
Author(s):  
Michel Andre L .Vinagreiro ◽  
Edson C. Kitani ◽  
Armando Antonio M. Lagana ◽  
Leopoldo R. Yoshioka
Keyword(s):  
Transfer Learning ◽  
Feature Space ◽  
Feature Maps ◽  
Traffic Sign ◽  
Real Time System ◽  
Acceleration Techniques ◽  
Sign Recognition ◽  
Speed Up ◽  
Fast Classification ◽  
Classification Time

Computer vision plays a crucial role in ADAS security and navigation, as most systems are based on deep CNN architectures the computational resource to run a CNN algorithm is demanding. Therefore, the methods to speed up computation have become a relevant research issue. Even though several works on acceleration techniques found in the literature have not yet been achieved satisfactory results for embedded real-time system applications. This paper presents an alternative approach based on the Multilinear Feature Space (MFS) method resorting to transfer learning from large CNN architectures. The proposed method uses CNNs to generate feature maps, although it does not work as complexity reduction approach. When the training process ends, the generated maps are used to create vector feature space. We use this new vector space to make projections of any new sample in order to classify them. Our method, named MFS-CNN, uses the transfer learning from pre trained CNN to reduce the classification time of new sample image, with minimal loss in accuracy. Our method uses the VGG-16 model as the base CNN architecture for experiments; however, the method works with any similar CNN model. Using the well-known Vehicle Image Database and the German Traffic Sign Recognition Benchmark we compared the classification time of original VGG-16 model with the MFS-CNN method and our method is, on average, 17 times faster. The fast classification time reduces the computational and memories demand in embedded applications that requires a large CNN architecture.

scholarly journals Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2684 ◽  
Author(s):  
Obed Tettey Nartey ◽  
Guowu Yang ◽  
Sarpong Kwadwo Asare ◽  
Jinzhao Wu ◽  
Lady Nadia Frempong
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Supervised Learning ◽  
Machine Learning Algorithms ◽  
Unbalanced Data ◽  
Traffic Sign Recognition ◽  
Training Set ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Weakly Supervised

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications.

Traffic Sign Recognition for Computer Vision Project-Based Learning

2013 ◽  
Vol 56 (3) ◽  
pp. 364-371 ◽  
Author(s):  
David Geronimo ◽  
Joan Serrat ◽  
Antonio M. Lopez ◽  
Ramon Baldrich
Keyword(s):  
Computer Vision ◽  
Project Based Learning ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition

scholarly journals Transfer Learning Based Traffic Sign Recognition System for Surveillance Application

2019 ◽  
Vol 8 (3) ◽  
pp. 573-576
Keyword(s):  
Transfer Learning ◽  
Road Traffic ◽  
Recognition System ◽  
Vehicle Tracking ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Wide Range ◽  
Traffic Recognition ◽  
Surveillance Application

Road Traffic Recognition is very important in many applications, such as automated deployment, traffic mapping, and vehicle tracking. Proposed traffic sign recognition system tails the transfer learning method that is frequently used in neural network uses. The benefit of expending this technique is that the initially network has been trained with a rich set of features appropriate to a wide range of images. Once the network is trained , learning can be transferred to the new activity adjustment to the network. Firsthand Indian traffic sign dataset is used.New results exhibit that the suggested method can accomplish modest outcomes when matched with other related techniques.

scholarly journals Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images

2021 ◽  
Vol 11 (7) ◽  
pp. 3155
Author(s):  
Guo-Shiang Lin ◽  
Kuan-Ting Lai ◽  
Jian-Ming Syu ◽  
Jen-Yung Lin ◽  
Sin-Kuo Chai
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Cross Validation ◽  
Normal Skin ◽  
Object Localization ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Subjective Testing ◽  
Instance Segmentation

In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FPS) rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images.

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