Ultra-thin MobileNet

Sinha, Debjyoti; El-Sharkawy, Mohamed

doi:10.1109/ccwc47524.2020.9031228

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Through our separable convolution and point-by-point convolution, we achieve the ascending dimension function of direct point-by-point convolution and introduce the information interaction between the local regions of the plane to increase its receptive field, which achieved by direct point-by-point convolution ( Chen & Su, 2019 ; Sinha & El-Sharkawy, 2020 ; Sinha & El-Sharkawy, 2019 ). Plane bottlenecks are often used in similar bottleneck layer structures, so separable convolution is reintroduced to extract the local plane features of data obtained from spatial dimension features.…”

Section: Methodsmentioning

confidence: 99%

PlaneNet: an efficient local feature extraction network

Lin

et al. 2021

PeerJ Computer Science

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Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 × 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 × 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub: https://github.com/LinB203/planenet.

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Section: Methodsmentioning

confidence: 99%

PlaneNet: an efficient local feature extraction network

Lin

et al. 2021

PeerJ Computer Science

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“…In recent literature, interest was growing in building small and efficient neural networks for mobile vision applications using modern deep learning models to perform visual servoing such as object detection. The SSD approach is based on a convolutional feed-forward network that produces a fixedsize collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum detection step to produce the final detections [31], [32]. In a recent study of W. Liu et al, the SSD algorithm is faster than YOLO and significantly more accurate than, in fact, slower techniques that perform explicit region proposals and pooling, such as Faster R-CNN [33].…”

Section: Mobilenet-ssdmentioning

confidence: 99%

Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

Rogelio¹,

Dadios

Vicerra

et al. 2022

Int. J. Adv. Intell. Informatics

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The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools.

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“…Sinha and others [30] proposed the reduced channel for the last DSC layer, which is less than 1024. Channel depth of 1024 is the value that proposed in MobileNet v1.…”

Section: Related Workmentioning

confidence: 99%

A novel MobileNet with selective depth multiplier to compromise complexity and accuracy

Kim

Heo

2022

ETRI Journal

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In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side. To reduce the computational complexity of CNNs for edge computing, a lightweight algorithm, such as a MobileNet, is proposed. Although MobileNet is lighter than other CNN models, it commonly achieves lower classification accuracy. Hence, to find a balance between complexity and accuracy, additional hyperparameters for adjusting the size of the model have recently been proposed. However, significantly increasing the number of parameters makes models dense and unsuitable for devices with limited computational resources. In this study, we propose a novel MobileNet architecture, in which the number of parameters is adaptively increased according to the importance of feature maps. We show that our proposed network achieves better classification accuracy with fewer parameters than the conventional MobileNet.

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Ultra-thin MobileNet

Cited by 6 publications

References 4 publications

PlaneNet: an efficient local feature extraction network

PlaneNet: an efficient local feature extraction network

Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

A novel MobileNet with selective depth multiplier to compromise complexity and accuracy

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