VISPNN: VGG-Inspired Stochastic Pooling Neural Network

Wang, Shuihua; Khan, Muhammad Attique; Zhang, Yudong

doi:10.32604/cmc.2022.019447

Cited by 30 publications

(12 citation statements)

References 23 publications

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“…e classification effects of a total of seven representative CNN standard models [15][16][17][18][19][20][21] (all with a single optimizer and a single loss function) containing AlexNet, GoogleNet, ResNet, VGG, EfficientNet, Mobile-Net, and ShuffleNet are collected during the determination of the numerical model for weld seam recognition, and the classification accuracy of each model is shown in Table 2, where ResNet is the model with the highest accuracy for weld seam recognition among all algorithms in the table . Based on the ResNet model and using the driven strategy in Figure 2, the classification accuracy obtained was higher (1.6% improved) compared to the plain ResNet model. 3 is the confusion matrix of the "ResNet + multistage training strategy model."…”

Section: Classification Resultsmentioning

confidence: 99%

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Shi

Sun

Liu

et al. 2022

Scientific Programming

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In terms of the problems of five categories of nonweld seam stripes, including inclusion, oil-spot, silk-spot, and water-spot, which interfere with weld seam recognition during robotic welding, a convolutional neural network (CNN) algorithm, combined with a multistage training strategy, is used to construct a digital model for weld seam recognition, on the basis of which the classification accuracy is compared with the standard model of seven categories of representative CNN. The results show that the ResNet model with a multistage training strategy classifies weld seams with an accuracy of 83.8%, which is superior to other standard models. In this study, the physical scenario of weld seam recognition is migrated to a neural network digital model, fulfilling the intelligent recognition of weld seams in complex scenarios based on the CNN digital model.

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Section: Classification Resultsmentioning

confidence: 99%

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Shi

Sun

Liu

et al. 2022

Scientific Programming

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“…Poor performance may be the result of information loss due to the large and rapid reduction of the feature maps. RNNPool [64,65] attempts to solve this problem using a recursive down sampling network. The first recurrent network highlights feature maps and the second recurrent network summarizes its results as pooling output.…”

Section: Methods To Preserve Critical Information When Poolingmentioning

confidence: 99%

A Comparison of Pooling Methods for Convolutional Neural Networks

et al. 2022

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One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.

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“…Max pooling is a commonly used pooling method employed to reduce the dimensions of feature maps. In the 3D context, max pooling operates across three dimensionswidth, height, and depth/time [34][35][36][37].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Emotion Recognition and Management in the Tourism Industry During Emergency Events Using Improved Convolutional Neural Network

Qi,

Han

2024

IEEE Access

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The purpose is to optimize emotion recognition capabilities in the tourism industry during emergency events and enhance management efficiency. Hence, based on the principles of emergency events and emotion recognition, this work has outlined the fundamental emotion recognition process. Additionally, an emotion recognition model is constructed based on a Convolutional Neural Network (CNN), and the process of extracting emotions using a three-dimensional (3D) CNN is proposed. Finally, an attention mechanism is employed to optimize the 3D CNN model, and a comparative analysis of accuracy and precision is conducted using the Bimodal Face and Body Gesture Database (FABO). The research findings reveal that the optimized 3D CNN model exhibits lower error rates in recognizing anxiety emotions, with only 3 samples going unrecognized. Its overall recognition performance is superior to other models. There are variations in the recognition accuracy among different models, but in general, the optimized 3D CNN model performs relatively well across various datasets, achieving recognition accuracies of 83.92%, 82.33%, and 88.81%. Compared to other models, the optimized 3D CNN model demonstrates higher precision in recognizing different emotions, particularly excelling in identifying anger, disgust, and happiness, with precision rates of 97%, 91%, and 94%, respectively. This work has improved the accuracy and efficiency of emotion recognition, providing more intelligent and effective support for emergency event management in the tourism industry.

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VISPNN: VGG-Inspired Stochastic Pooling Neural Network

Cited by 30 publications

References 23 publications

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

Research on Weld Seam Bead Recognition Based on Convolution Neural Network

A Comparison of Pooling Methods for Convolutional Neural Networks

Emotion Recognition and Management in the Tourism Industry During Emergency Events Using Improved Convolutional Neural Network

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