Aggressive Action Recognition Using 3D CNN Architectures

Saveliev, Anton; Uzdiaev, Mikhail; Malov, Dmitrii

doi:10.1109/dese.2019.00165

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Ji et al [41] used 3DCNN in airport monitoring. Saveliev et al [42] obtained a good recognition rate in 3DCNN activity recognition in real-time. The model had the best accuracy with a 3x3x3 kernel.…”

Section: D-cnnmentioning

confidence: 97%

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

Chen¹,

Kuo²,

Zhang³

et al. 2023

Preprint

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In Taiwan, peanuts are classified by hand. The kernel quality is judged based on the appearance of peanuts. The identification takes considerable time and labor. Fatigue can induce misrecogni-tion or grade cognition instability due. Peanut defects are generally divided into health, underde-velopment, insect bite, and rupture. This study employed two imaging methods push-boom FX10 and Snapshot. Deep learning was used to detect peanut defects. A push-broom instrument was used for testing. There were 1,560 peanuts, including 240 good and 240 bad ones. There were a total of 1,080 good peanuts and bad peanuts in the test set data, the band selection was used, and a CNN model was built. Based on the results from three methods, 3DCNN could classify with 97%accuracy. The Snapshot was used to achieve the real-time design of a lightweight CNN model. Finally, five bands were selected using PCA, and the screening could be accurate and efficient. The maximum overall accuracy was about 98.5%. Kappa was 97.3% with real-time recognition. This will be a major advantage in the future practical application and commercialization processes. This technique can reduce labor costs, achieve intelligent detection and intelligent grading, and bring breakthroughs to smart agriculture for other crops as well.

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Section: D-cnnmentioning

confidence: 97%

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

Chen¹,

Kuo²,

Zhang³

et al. 2023

Preprint

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“…Convolutional neural networks [ 42 , 43 , 44 , 45 ] have achieved superior performance in many visual tasks, such as object classification and detection. Convolutional neural networks learn abstract features and concepts from raw image pixels [ 46 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Architecture Reduction for fMRI Data

Alvarez-Gonzalez

Méndez-Vázquez

2022

Brain Sciences

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In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. Thus, understanding the feature extraction process provides insights to help optimize pre-trained architectures, better generalize the models, and obtain the context of each layer’s features. In this work, we aim to improve feature extraction through the use of a texture amortization map (TAM). An algorithm was developed to obtain characteristics from the filters amortizing the filter’s effect depending on the texture of the neighboring pixels. From the initial algorithm, a novel geometric classification score (GCS) was developed, in order to obtain a measure that indicates the effect of one class on another in a classification problem, in terms of the complexity of the learnability in every layer of the deep learning architecture. For this, we assume that all the data transformations in the inner layers still belong to a Euclidean space. In this scenario, we can evaluate which layers provide the best transformations in a CNN, allowing us to reduce the weights of the deep learning architecture using the geometric hypothesis.

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“…It has a highly similar structure with 2DCNN, but their difference is that 2DCNN uses the 2D convolution kernel, while 3DCNN uses the 3D convolution kernel. Three-dimensional CNN [55][56][57][58] can simultaneously extract spatial and depth features for three-dimensional data via the 3D convolution kernel.…”

Section: Input Imagementioning

confidence: 99%

Hyperspectral Image Classification Based on Two-Branch Spectral–Spatial-Feature Attention Network

Wang

et al. 2021

Remote Sensing

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Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods.

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Aggressive Action Recognition Using 3D CNN Architectures

Cited by 12 publications

References 30 publications

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

The Development of Peanut Defect Detection Technique Using Hyperspectral Images

Deep Learning Architecture Reduction for fMRI Data

Hyperspectral Image Classification Based on Two-Branch Spectral–Spatial-Feature Attention Network

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