Browsing by Subject "Convolutional Neural Networks"

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    Design Space Exploration of Convolutional Neural Networks for Image Classification
    (2020-12) Shah, Prasham; Mohamed, El-Sharkawy; King, Brian; Rizkalla, Maher
    Computer vision is a domain which deals with the goal of making technology as efficient as human vision. To achieve that goal, after decades of research, researchers have developed algorithms that are able to work efficiently on resource constrained hardware like mobile or embedded devices for computer vision applications. Due to their constant efforts, such devices have become capable for tasks like Image Classification, Object Detection, Object Recognition, Semantic Segmentation, and many other applications. Autonomous systems like self-driving cars, Drones and UAVs, are being successfully developed because of these advances in AI. Deep Learning, a part of AI, is a specific domain of Machine Learning which focuses on developing algorithms for such applications. Deep Learning deals with tasks like extracting features from raw image data, replacing pipelines of specialized models with single end-to-end models, making models usable for multiple tasks with superior performance. A major focus is on techniques to detect and extract features which provide better context for inference about an image or video stream. A deep hierarchy of rich features can be learned and automatically extracted from images, provided by the multiple deep layers of CNN models. CNNs are the backbone of Computer Vision. The reason that CNNs are the focus of attention for deep learning models is that they were specifically designed for image data. They are complicated but very effective in extracting features from an image or a video stream. After AlexNet won the ILSVRC in 2012, there was a drastic increase in research related with CNNs. Many state-of-the-art architectures like VGG Net, GoogleNet, ResNet, Inception-v4, Inception-Resnet-v2, ShuffleNet, Xception, MobileNet, MobileNetV2, SqueezeNet, SqueezeNext and many more were introduced. The trend behind the research depicts an increase in the number of layers of CNN to make them more efficient but with that, the size of the model increased as well. This problem was fixed with the advent of new algorithms which resulted in a decrease in model size. As a result, today we have CNN models, which are implemented on mobile devices. These mobile models are compact and have low latency, which in turn reduces the computational cost of the embedded system. This thesis resembles similar idea, it proposes two new CNN architectures, A-MnasNet and R-MnasNet, which have been derived from MnasNet by Design Space Exploration. These architectures outperform MnasNet in terms of model size and accuracy. They have been trained and tested on CIFAR-10 dataset. Furthermore, they were implemented on NXP Bluebox 2.0, an autonomous driving platform, for Image Classification.
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    Design Space Exploration of Convolutional Neural Networks for Image Classification
    (2020-12) Shah, Prasham; Mohamed, El-Sharkawy; King, Brian; Rizkalla, Maher
    Computer vision is a domain which deals with the goal of making technology as efficient as human vision. To achieve that goal, after decades of research, researchers have developed algorithms that are able to work efficiently on resource constrained hardware like mobile or embedded devices for computer vision applications. Due to their constant efforts, such devices have become capable for tasks like Image Classification, Object Detection, Object Recognition, Semantic Segmentation, and many other applications. Autonomous systems like self-driving cars, Drones and UAVs, are being successfully developed because of these advances in AI. Deep Learning, a part of AI, is a specific domain of Machine Learning which focuses on developing algorithms for such applications. Deep Learning deals with tasks like extracting features from raw image data, replacing pipelines of specialized models with single end-to-end models, making models usable for multiple tasks with superior performance. A major focus is on techniques to detect and extract features which provide better context for inference about an image or video stream. A deep hierarchy of rich features can be learned and automatically extracted from images, provided by the multiple deep layers of CNN models. CNNs are the backbone of Computer Vision. The reason that CNNs are the focus of attention for deep learning models is that they were specifically designed for image data. They are complicated but very effective in extracting features from an image or a video stream. After AlexNet won the ILSVRC in 2012, there was a drastic increase in research related with CNNs. Many state-of-the-art architectures like VGG Net, GoogleNet, ResNet, Inception-v4, Inception-Resnet-v2, ShuffleNet, Xception, MobileNet, MobileNetV2, SqueezeNet, SqueezeNext and many more were introduced. The trend behind the research depicts an increase in the number of layers of CNN to make them more efficient but with that, the size of the model increased as well. This problem was fixed with the advent of new algorithms which resulted in a decrease in model size. As a result, today we have CNN models, which are implemented on mobile devices. These mobile models are compact and have low latency, which in turn reduces the computational cost of the embedded system. This thesis resembles similar idea, it proposes two new CNN architectures, A-MnasNet and R-MnasNet, which have been derived from MnasNet by Design Space Exploration. These architectures outperform MnasNet in terms of model size and accuracy. They have been trained and tested on CIFAR-10 dataset. Furthermore, they were implemented on NXP Bluebox 2.0, an autonomous driving platform, for Image Classification.
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    Efficient Edge Intelligence in the Era of Big Data
    (2021-08) Wong, Jun Hua; Zhang, Qingxue; King, Brian; Schubert, Peter
    Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attentions. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. Targeting this obstacle, we propose to investigate the biodynamic patterns in the data and design a data-driven approach for intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original energy need. Further, compared with state-of-the-art wavelet compression-based method, our method can compress the data with a dramatically lower error for a similar energy budget. Our experiments and the validated approach are expected to boost the energy efficiency of wearables, and thus greatly advance ubiquitous big data applications in era of smart health. In recent years, there has also been a growing interest in edge intelligence for emerging instantaneous big data inference. However, the inference algorithms, especially deep learning, usually require heavy computation requirements, thereby greatly limiting their deployment on the edge. We take special interest in the smart health wearable big data mining and inference. Targeting the deep learning’s high computational complexity and large memory and energy requirements, new approaches are urged to make the deep learning algorithms ultra-efficient for wearable big data analysis. We propose to leverage knowledge distillation to achieve an ultra-efficient edge-deployable deep learning model. More specifically, through transferring the knowledge from a teacher model to the on-edge student model, the soft target distribution of the teacher model can be effectively learned by the student model. Besides, we propose to further introduce adversarial robustness to the student model, by stimulating the student model to correctly identify inputs that have adversarial perturbation. Experiments demonstrate that the knowledge distillation student model has comparable performance to the heavy teacher model but owns a substantially smaller model size. With adversarial learning, the student model has effectively preserved its robustness. In such a way, we have demonstrated the framework with knowledge distillation and adversarial learning can, not only advance ultra-efficient edge inference, but also preserve the robustness facing the perturbed input.
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    Efficientnext: Efficientnet For Embedded Systems
    (2022-05) Deokar, Abhishek; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    Convolutional Neural Networks have come a long way since AlexNet. Each year the limits of the state of the art are being pushed to new levels. EfficientNet pushed the performance metrics to a new high and EfficientNetV2 even more so. Even so, architectures for mobile applications can benefit from improved accuracy and reduced model footprint. The classic Inverted Residual block has been the foundation upon which most mobile networks seek to improve. EfficientNet architecture is built using the same Inverted Residual block. In this thesis we experiment with Harmonious Bottlenecks in place of the Inverted Residuals to observe a reduction in the number of parameters and improvement in accuracy. The designed network is then deployed on the NXP i.MX 8M Mini board for Image classification.
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    Improving Object Detection using Enhanced EfficientNet Architecture
    (2023-08) Kamel Ibrahim, Michael; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    EfficientNet is designed to achieve top accuracy while utilizing fewer parameters, in addition to less computational resources compared to previous models. In this paper, we are presenting compound scaling method that re-weight the network’s width (w), depth(d), and resolution (r), which leads to better performance than traditional methods that scale only one or two of these dimensions by adjusting the hyperparameters of the model. Additionally, we are presenting an enhanced EfficientNet Backbone architecture. We show that EfficientNet achieves top accuracy on the ImageNet dataset, while being up to 8.4x smaller and up to 6.1x faster than previous top performing models. The effec- tiveness demonstrated in EfficientNet on transfer learning and object detection tasks, where it achieves higher accuracy with fewer parameters and less computation. Henceforward, the proposed enhanced architecture will be discussed in detail and compared to the original architecture. Our approach provides a scalable and efficient solution for both academic research and practical applications, where resource constraints are often a limiting factor.
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    Multi-spectral Fusion for Semantic Segmentation Networks
    (2023-05) Edwards, Justin; El-Sharkawy, Mohamed; King, Brian; Kim, Dongsoo
    Semantic segmentation is a machine learning task that is seeing increased utilization in multiples fields, from medical imagery, to land demarcation, and autonomous vehicles. Semantic segmentation performs the pixel-wise classification of images, creating a new, seg- mented representation of the input that can be useful for detected various terrain and objects within and image. Recently, convolutional neural networks have been heavily utilized when creating neural networks tackling the semantic segmentation task. This is particularly true in the field of autonomous driving systems. The requirements of automated driver assistance systems (ADAS) drive semantic seg- mentation models targeted for deployment on ADAS to be lightweight while maintaining accuracy. A commonly used method to increase accuracy in the autonomous vehicle field is to fuse multiple sensory modalities. This research focuses on leveraging the fusion of long wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent perfor- mance gaps when using visual imagery alone. This comes with a host of benefits, such as increase performance in various lighting conditions and adverse environmental conditions. Utilizing this fusion technique is an effective method of increasing the accuracy of a semantic segmentation model. Being a lightweight architecture is key for successful deployment on ADAS, as these systems often have resource constraints and need to operate in real-time. Multi-Spectral Fusion Network (MFNet) [1] accomplishes these parameters by leveraging a sensory fusion approach, and as such was selected as the baseline architecture for this research. Many improvements were made upon the baseline architecture by leveraging a variety of techniques. Such improvements include the proposal of a novel loss function categori- cal cross-entropy dice loss, introduction of squeeze and excitation (SE) blocks, addition of pyramid pooling, a new fusion technique, and drop input data augmentation. These improve- ments culminated in the creation of the Fast Thermal Fusion Network (FTFNet). Further improvements were made by introducing depthwise separable convolutional layers leading to lightweight FTFNet variants, FTFNet Lite 1 & 2. 13 The FTFNet family was trained on the Multi-Spectral Road Scenarios (MSRS) and MIL- Coaxials visual/LWIR datasets. The proposed modifications lead to an improvement over the baseline in mean intersection over union (mIoU) of 2.92% and 2.03% for FTFNet and FTFNet Lite 2 respectively when trained on the MSRS dataset. Additionally, when trained on the MIL-Coaxials dataset, the FTFNet family showed improvements in mIoU of 8.69%, 4.4%, and 5.0% for FTFNet, FTFNet Lite 1, and FTFNet Lite 2.
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    Object Detection Using Vision Transformed EfficientDet
    (2023-08) Kar, Shreyanil; El-Sharkawy, Mohamed A.; King, Brian S.; Rizkalla, Maher E.
    This research presents a novel approach for object detection by integrating Vision Transformers (ViT) into the EfficientDet architecture. The field of computer vision, encompassing artificial intelligence, focuses on the interpretation and analysis of visual data. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have significantly improved the accuracy and efficiency of computer vision systems. Object detection, a widely studied application within computer vision, involves the identification and localization of objects in images. The ViT backbone, renowned for its success in image classification and natural language processing tasks, employs self-attention mechanisms to capture global dependencies in input images. However, ViT’s capability to capture fine-grained details and context information is limited. To address this limitation, the integration of ViT into the EfficientDet architecture is proposed. EfficientDet is recognized for its efficiency and accuracy in object detection. By combining the strengths of ViT and EfficientDet, the proposed integration enhances the network’s ability to capture fine-grained details and context information. It leverages ViT’s global dependency modeling alongside EfficientDet’s efficient object detection framework, resulting in highly accurate and efficient performance. Noteworthy object detection frameworks utilized in the industry, such as RetinaNet, EfficientNet, and EfficientDet, primarily employ convolution. Experimental evaluations were conducted using the PASCAL VOC 2007 and 2012 datasets, widely acknowledged benchmarks for object detection. The integrated ViT-EfficientDet model achieved an impressive mean Average Precision (mAP) score of 86.27% when tested on the PASCAL VOC 2007 dataset, demonstrating its superior accuracy. These results underscore the potential of the proposed integration for real-world applications. In conclusion, the research introduces a novel integration of Vision Transformers into the EfficientDet architecture, yielding significant improvements in object detection performance. By combining ViT’s ability to capture global dependencies with EfficientDet’s efficiency and accuracy, the proposed approach offers enhanced object detection capabilities. Future research directions may explore additional datasets and evaluate the performance of the proposed framework across various computer vision tasks.
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    Squeeze and Excite Residual Capsule Network for Embedded Edge Devices
    (2022-08) Naqvi, Sami; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    During recent years, the field of computer vision has evolved rapidly. Convolutional Neural Networks (CNNs) have become the chosen default for implementing computer vision tasks. The popularity is based on how the CNNs have successfully performed the well-known computer vision tasks such as image annotation, instance segmentation, and others with promising outcomes. However, CNNs have their caveats and need further research to turn them into reliable machine learning algorithms. The disadvantages of CNNs become more evident as the approach to breaking down an input image becomes apparent. Convolutional neural networks group blobs of pixels to identify objects in a given image. Such a technique makes CNNs incapable of breaking down the input images into sub-parts, which could distinguish the orientation and transformation of objects and their parts. The functions in a CNN are competent at learning only the shift-invariant features of the object in an image. The discussed limitations provides researchers and developers a purpose for further enhancing an effective algorithm for computer vision. The opportunity to improve is explored by several distinct approaches, each tackling a unique set of issues in the convolutional neural network’s architecture. The Capsule Network (CapsNet) which brings an innovative approach to resolve issues pertaining to affine transformations by sharing transformation matrices between the different levels of capsules. While, the Residual Network (ResNet) introduced skip connections which allows deeper networks to be more powerful and solves vanishing gradient problem. The motivation of these fusion of these advantageous ideas of CapsNet and ResNet with Squeeze and Excite (SE) Block from Squeeze and Excite Network, this research work presents SE-Residual Capsule Network (SE-RCN), an efficient neural network model. The proposed model, replaces the traditional convolutional layer of CapsNet with skip connections and SE Block to lower the complexity of the CapsNet. The performance of the model is demonstrated on the well known datasets like MNIST and CIFAR-10 and a substantial reduction in the number of training parameters is observed in comparison to similar neural networks. The proposed SE-RCN produces 6.37 Million parameters with an accuracy of 99.71% on the MNIST dataset and on CIFAR-10 dataset it produces 10.55 Million parameters with 83.86% accuracy.