Performance estimation of the state-of-the-art convolution neural networks for thermal images-based gender classification system

Farooq, Muhammad Ali; Javidnia, Hossein; Corcoran, Peter

doi:10.1117/1.jei.29.6.063004

Cited by 8 publications

(9 citation statements)

References 43 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the vast compute and time resources required to develop neural network models, it is a popular approach in deep learning where pretrained models are used as the starting point on computer vision and natural language processing tasks. 24 There are three ways of fine tuning the modelusing the model as a feature extractor, using the model to initialize weights and train the model again or freezing certain layers and retrain the higher layers. Since the dataset used here is small and the similarity between the data of CROWDO based AlexNet model and this model is high, only the denser layers and the final softmax layer are modified.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Transfer learning is a machine learning technique in which a model created for one problem is employed as the basis for a model on a different task. Given the vast compute and time resources required to develop neural network models, it is a popular approach in deep learning where pretrained models are used as the starting point on computer vision and natural language processing tasks 24 . A concept of self-training-based pretraining is employed in the proposed model to improve the learning time.…”

Section: Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Indu

Priyadharsini

2023

J. Electron. Imag.

View full text Add to dashboard Cite

.Automated plant disease identification is one of the significant tasks in the agricultural sector. With the advent of deep-learning models, classification and segmentation have been completely automated using a convolutional neural network (CNN). This paper proposes the UV-Net model, with a recurrent encoder and an up-sampling decoder for multiclass image segmentation and generation of masks. The improvement of the segmentation model is the stacking of conv2D layers before the down convolution operation, concatenation of features extracted from the convolution process, up-sampling along with transpose convolution in the expansion path, and exponential linear unit (ELU) activation. This model is combined with an optimized, pretrained, and self-trained CNN for generating the classes of diseased leaves. The classification is further enhanced by concatenating the high-level features extracted from the pretrained CNN model along with the low-level features from the encoder part of the UV-Net model. The features are concatenated using the hybrid wind-driven particle swarm optimized (WDPSO) deep feature selective concatenation mechanism (DFSCM). The proposed model is trained and the performance is tested on multiclass diseases of tomato plants like bacterial spot, early blight, leaf mold, and target spot. The proposed architecture is also compared with some of the well-known CNN models such as FCN, FPN, U-Net, V-Net, and Mask R-CNN. The model proves to be a robust system for detecting diseases from images of varying sizes, shapes, and lighting. The proposed model achieved an average training accuracy of 98.20% with a dice coefficient of 0.8938 and an Intersection over union coefficient (IoU) of 0.7458. The model also generates accurate masks from which the intensity and pattern of diseases could be identified for earlier prevention and treatment of diseases.

show abstract

Section: Transfer Learningmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Indu

Priyadharsini

2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…In this research, the convolution neural network (CNN) has attained state-of-the-art performance. However, there are still issues with CNN training's overall optimization [14], [22], [23]. The CNN used in this study uses cloud images that have been pre-processed.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The VGG19 and DenseNet201 transfer learning models were tested in this research. 2) DenseNet201 Model: The DenseNet201 model is also a member of the DenseNet family of image classification models [28] [29]. The primary difference between this model and the DenseNet201 model is the model's size and accuracy.…”

Section: Feature Extractionmentioning

confidence: 99%

Improving Accuracy of Cloud Images using DenseNet-VGG19

Fitriana¹,

Arifa²,

Prasetiadi³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Weather classification has become a significant challenge due to the unpredictable nature of climate conditions. For farmers, predicting the start of the rainy season is very important. This is because it is related to the cost factor that must be incurred, and also, the waiting time for the harvest has an effect if the weather is not supportive. Farmers also have to prepare seeds for the start of their farming. Therefore, farmers who start nurseries early in the rainy season will miss significant planting time. Based on these problems, this study uses a convolutional neural network (CNN) for weather classification using cloud imagery. CNN is shown to classify different spectro-temporal features of sound and is thus suitable for cloud image classification. We collect cloud image data using secondary data. Our model used a layer based on the convolution CNN architecture with a pooling layer and a solid layer as the output layer. The cloud dataset used is 1230 data divided into five classes, namely cloudy, foggy, rain, shine, and sunrise, which we use to train our model in research for the feature extraction process using DenseNet and VGG19. We use two types of classification, namely fully connected and Global Average Pooling (GAP). Our model can achieve a classification accuracy of 90.8% DenseNet-Fully Connected from our training process. From our testing process, our model can reach 95.7% using DenseNet-Fully Connected classification accuracy. Thus, the CNN model proved very accurate in classifying cloud images.

show abstract

“…The ODU Vision Lab has ongoing work in gender classification and person classification using optical and infrared special purpose sensors [1][2][3][4][5]. Infrared sensors compliment the abilities of visual range sensors for human subject analysis including face recognition [6][7][8][9], action recognition [10][11][12][13][14], and gender [15][16][17][18][19][20] and identity [20][21][22][23][24][25][26] recognition. These sensors may be deployed in the field with a small amount of training data available to establish recognition models.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning using infrared and optical full motion video data for gender classification

Glandon

Zalameda

Iftekharuddin

2023

Infrared Technology and Applications XLIX

View full text Add to dashboard Cite

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

show abstract

Performance estimation of the state-of-the-art convolution neural networks for thermal images-based gender classification system

Cited by 8 publications

References 43 publications

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Improving Accuracy of Cloud Images using DenseNet-VGG19

Transfer learning using infrared and optical full motion video data for gender classification

Contact Info

Product

Resources

About