Area Optimized CNN Architecture Using Folding Approach

Amin, Mukhtar; Adiono, Trio

doi:10.1109/iceei47359.2019.8988879

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…For CNNs, convolutional layers and pooling layers are the most critical components. Convolutional layers perform point-wise multiplication operations on different regions of the input data using a sliding window of convolutional kernels [10], which can be viewed as a filtering process. Through this process, convolutional layers can detect various features.…”

Section: Cnnmentioning

confidence: 99%

Face Mask Classification Based on the Convolutional Neural Network

2024

HSET

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The worldwide emergence of the COVID-19 pandemic has instigated widespread anxiety. In an effort to curb the virus's transmission, the adoption of mask-wearing has steadily become more prevalent. Numerous public facilities now require individuals to wear masks upon entry and exit. Within this framework, the effective and economical identification of mask-wearing individuals has become a growing priority, and deep learning has demonstrated its capability to address this need. Utilizing a dataset consisting of 440 provided images, the current experiment employed a Convolutional Neural Network (CNN) model to determine the presence or absence of masks on individuals. The experimental process encompassed dataset segmentation, CNN model construction, model training, and performance evaluation with the objective of attaining the best-performing model. The research revealed that, within a specific range, an increase in the number of layers corresponded to an enhancement in the CNN's performance. Remarkably, at 13 layers, the CNN achieved its highest accuracy, reaching 93.18%, with a recorded lowest loss value of 0.1608. Despite this, the CNN model's accuracy decreased as a result of an increase in the convolutional kernel size, which was accompanied by variations in the loss value. After multiple iterations of experimentation, the optimal convolutional kernel size was determined to be 3x3. In the quest for a more effective means of detecting mask compliance in public spaces, this study underscores the potential of deep learning techniques, particularly CNNs, and highlights the significance of carefully configuring model architecture for optimal results.

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Section: Cnnmentioning

confidence: 99%

Face Mask Classification Based on the Convolutional Neural Network

2024

HSET

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“…Image data processing is one of the earliest fields in which deep learning algorithms are applied. In 1989, Lecun et al proposed the concept of CNN [8]. With the rapid development of artificial intelligence and deep learning technology in the past ten years, CNN shows great advantages in image data processing, and is proved to be more suitable for learning and expression of image features [9,10].…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Yang

et al. 2021

IEEE Access

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Electrical discharge is a sign of insulation damage of power equipment and an essential part of power inspection. For the recognition of plasma discharge patterns, the accuracy rate of traditional manual inspection relies heavily on the experience of the staff, and misjudgment is inevitable. The emerging method based on manual feature extraction of discharge signals also has its limitations. Thus, a high-accurate recognition method based on convolutional neural network (CNN) and visible images is proposed in this paper. Under the deep learning framework-TensorFlow, CNN model is established and pre-trained weights are loaded through transfer learning. The visible image dataset used for training and evaluation includes four discharge patterns: arc discharge, corona discharge, creeping discharge and plasma jet. In data processing, data amplification and data enhancement are used to expand the sample. Besides, typical network models with fully connected layers are adopted and compared. Three optimization methods of ReLU, BN and Dropout are applied to solve the overfitting problem, and the corresponding effects are discussed in the model of VGG16 and ResNet152. The test accuracy of the optimized VGG16 reaches 99.6% without overfitting, which indicates the promising potential in the recognition of plasma discharge patterns in power inspection. INDEX TERMS electrical discharge, plasma discharge patterns, visible images, image recognition, convolutional neural network, transfer learningThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Area Optimized CNN Architecture Using Folding Approach

Cited by 2 publications

References 8 publications

Face Mask Classification Based on the Convolutional Neural Network

Face Mask Classification Based on the Convolutional Neural Network

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

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