A better way to monitor haze through image based upon the adjusted LeNet-5 CNN model

Fan, Yonglei; Rui, Xiaoping; Poslad, Stefan; Zhang, Guangyuan; Tian, Yu; Xu, Xijie; Song, Xianfeng

doi:10.1007/s11760-019-01574-6

Cited by 13 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The functions of convolutional layers like feature extractors aim to learn feature representations of input images. Convolutional layers consist of multiple convolution kernels to obtain local information at different locations on the image [ 14 ]. The functions of convolutional layer, pooling layer, and fully connected layer can be used to extract face recognition features and facial expression information.…”

Section: Methodsmentioning

confidence: 99%

Application of Higher Education Management in Colleges and Universities by Deep Learning

Yao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The development of artificial intelligence (AI) has brought great convenience to people and has been widely used in the field of education. To monitor the classroom status of college students in real time and achieve the purpose of balanced distribution of educational resources, the facial expression recognition (FER) algorithm is applied to the management of higher education in universities. Firstly, the convolutional neural network (CNN) is studied in depth, and secondly, the process and method of FER are explored in detail, and an adaptive FER algorithm based on the differential convolutional neural network (DCNN) is constructed. Finally, the algorithm is applied to the CK + database and the BU-4DFE database. The results manifest that the designed algorithm has an accuracy of 99.02% for keyframe detection in the CK + database and 98.35% for the BU-4DFE database. The algorithm has a high accuracy of keyframe detection for both expression databases. It has a good effect on the automatic detection of keyframes of expression sequences and can reach a level similar to that of manual frame selection. Compared with the existing algorithms, the proposed method still has higher advantages. It can effectively eliminate the interference of individual differences and environmental noise on FER. Experiments reveal that the proposed FER algorithm DCNN-based has a good recognition effect and is suitable for monitoring students’ classroom status. This research has certain reference significance for the application of AI in higher education management in colleges and universities.

show abstract

Section: Methodsmentioning

confidence: 99%

Application of Higher Education Management in Colleges and Universities by Deep Learning

Yao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…For example, In reference [15], the recognition of haze images was performed by adjusting the parameters and structure of the classic LeNet-5 model. The image recognition technology was applied to a haze image field, which showed good performance.…”

Section: Fig 2 Principle Diagram Of Convolution Operationmentioning

confidence: 99%

A novel deep LeNet-5 convolutional neural network model for image recognition

Zhang

Lei

et al. 2022

COMSIS J

View full text Add to dashboard Cite

At present, the traditional machine learning methods and convolutional neural network (CNN) methods are mostly used in image recognition. The feature extraction process in traditional machine learning for image recognition is mostly executed by manual, and its generalization ability is not strong enough. The earliest convolutional neural network also has many defects, such as high hardware requirements, large training sample size, long training time, slow convergence speed and low accuracy. To solve the above problems, this paper proposes a novel deep LeNet-5 convolutional neural network model for image recognition. On the basis of Lenet-5 model with the guaranteed recognition rate, the network structure is simplified and the training speed is improved. Meanwhile, we modify the Logarithmic Rectified Linear Unit (L_ReLU) of the activation function. Finally, the experiments are carried out on the MINIST character library to verify the improved network structure. The recognition ability of the network structure in different parameter s is analyzed compared with the state-of-the-art recognition algorithms. In terms of the recognition rate, the proposed method has exceeded 98%. The results show that the accuracy of the proposed structure is significantly higher than that of the other recognition algorithms, which provides a new reference for the current image recognition.

show abstract

“…There have been many studies focused on improving LeNet-5 algorithms. For example, Fan et al [34] monitored haze through images by adjusting the parameters and structure of the classical LeNet-5 Model. Zhang et al [35] proposed the TSR algorithm, which was based on an improved LeNet-5 algorithm, for situations when traditional computer vision recognition technology cannot meet real-time requirements.…”

Section: Data Availability Statementmentioning

confidence: 99%

Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

Jiang

Tao

2022

Atmosphere

View full text Add to dashboard Cite

In this paper, a novel typhoon intensity classification and estimation network (TICAENet) is constructed to recognize typhoon intensity. The TICAENet model is based on the LeNet-5 model, which uses weight sharing to reduce the number of training parameters, and the VGG16 model, which replaces a large convolution kernel with multiple small kernels to improve feature extraction. Satellite cloud images of typhoons over the Northwest Pacific Ocean and the South China Sea from 1995–2020 are taken as samples. The results show that the classification accuracy of this model is 10.57% higher than that of the LeNet-5 model; the classification accuracy of the TICAENet model is 97.12%, with a classification precision of 97.00% for tropical storms, severe tropical storms and super typhoons. The mean absolute error (MAE) and root mean square error (RMSE) of the samples estimation in 2019 are 4.78 m/s and 6.11 m/s, and the estimation accuracy are 18.98% and 20.65% higher than that of the statistical method, respectively. Additionally, the model takes less memory and runs faster due to the weight sharing and multiple small kernels. The results show that the proposed model performs better than other methods. In general, the proposed model can be used to accurately classify typhoon intensity and estimate the maximum wind speed by extracting features from geostationary meteorological satellite images.

show abstract

A better way to monitor haze through image based upon the adjusted LeNet-5 CNN model

Cited by 13 publications

References 26 publications

Application of Higher Education Management in Colleges and Universities by Deep Learning

Application of Higher Education Management in Colleges and Universities by Deep Learning

A novel deep LeNet-5 convolutional neural network model for image recognition

Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

Contact Info

Product

Resources

About