Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network

Aung, Htet; Bobkov, A.; Tun, Nyan Lin

doi:10.1109/icieam51226.2021.9446291

Cited by 27 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Adaptive momentum (Adam) optimization algorithm is an extension of stochastic gradient descent method, which is widely used in computer vision and natural language processing for deep learning applications. Adam combines the optimal performance of adaptive gradient (AdaGrad) and root mean squared propagation (RMSProp) algorithms [30], and it also provides an optimization method to solve sparse gradient and noise problems. The Adam algorithm integrates the first-order momentum of SGD and the second-order momentum of RMSProp.…”

Section: Selection Of Optimization Algorithmsmentioning

confidence: 99%

Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

Su¹,

Yuan²,

Wang³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

With the rapid development of Internet technology, the type of information in the Internet is extremely complex, and a large number of riot contents containing bloody, violent and riotous components have appeared. These contents pose a great threat to the network ecology and national security. As a result, the importance of monitoring riotous Internet activity cannot be overstated. Convolutional Neural Network (CNN-based) target detection algorithm has great potential in identifying rioters, so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5 (YOLOv5), and further optimization of hyperparameters using genetic algorithm to achieve fine-grained recognition of riot image content. First, the fine-grained features of riot-related images were identified, and then the dataset was constructed by manual annotation. Second, the training and testing work was carried out on the constructed dedicated dataset by supervised deep learning training. The research results have shown that the improved YOLOv5 network significantly improved the fine-grained feature extraction capability of riot-related images compared with the original YOLOv5 network structure, and the mean average precision (mAP) value was improved to 0.6128. Thus, it provided strong support for combating riot-related organizations and maintaining the online ecological environment.

show abstract

Section: Selection Of Optimization Algorithmsmentioning

confidence: 99%

Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

Su¹,

Yuan²,

Wang³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Panthakkan, S. M. Aznar used X-rays of lungs and VGG16 for the prediction of COVID-19 into binary classes, i.e., positive COVID-19 and negative COVID-19 [50]. H. Aung also used VGG16 with the combination of the YOLO algorithm to detect the face from real-time live video [51]. However, VGG16 faced an issue of vanishing gradient.…”

Section: Vgg16mentioning

confidence: 99%

SCDet: A Robust Approach for the Detection of Skin Lesions

et al. 2023

View full text Add to dashboard Cite

Red, blue, white, pink, or black spots with irregular borders and small lesions on the skin are known as skin cancer that is categorized into two types: benign and malignant. Skin cancer can lead to death in advanced stages, however, early detection can increase the chances of survival of skin cancer patients. There exist several approaches developed by researchers to identify skin cancer at an early stage, however, they may fail to detect the tiniest tumours. Therefore, we propose a robust method for the diagnosis of skin cancer, namely SCDet, based on a convolutional neural network (CNN) having 32 layers for the detection of skin lesions. The images, having a size of 227 × 227, are fed to the image input layer, and then pair of convolution layers is utilized to withdraw the hidden patterns of the skin lesions for training. After that, batch normalization and ReLU layers are used. The performance of our proposed SCDet is computed using the evaluation matrices: precision 99.2%; recall 100%; sensitivity 100%; specificity 99.20%; and accuracy 99.6%. Moreover, the proposed technique is compared with the pre-trained models, i.e., VGG16, AlexNet, and SqueezeNet and it is observed that SCDet provides higher accuracy than these pre-trained models and identifies the tiniest skin tumours with maximum precision. Furthermore, our proposed model is faster than the pre-trained model as the depth of its architecture is not too high as compared to pre-trained models such as ResNet50. Additionally, our proposed model consumes fewer resources during training; therefore, it is better in terms of computational cost than the pre-trained models for the detection of skin lesions.

show abstract

“…In this proposal, the human-detected frames are trained by the VGG-16 pre-trained CNN (convolutional neural network) [38] model. Using a multi-class classification problem, the frames are categorized into three classes: 0 for False human predicted (FHP), 1 for True human predicted (THP), and 2 for Without human identification (WH) frames.…”

Section: Vgg-16 Convolutional Neural Networkmentioning

confidence: 99%

An Enhanced Spatio-Temporal Human Detected Keyframe Extraction

D.,

Priscilla C.

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Due to the immense availability of Closed-Circuit Television surveillance, it is quite difficult for crime investigation due to its huge storage and complex background. Content-based video retrieval is an excellent method to identify the best Keyframes from these surveillance videos. As the crime surveillance reports numerous action scenes, the existing keyframe extraction is not exemplary. At this point, the Spatio-temporal Histogram of Oriented Gradients - Support Vector Machine feature method with the combination of Background Subtraction is appended over the recovered crime video to highlight the human presence in surveillance frames. Additionally, the Visual Geometry Group trains these frames for the classification report of human-detected frames. These detected frames are processed to extract the keyframe by manipulating an inter-frame difference with its threshold value to favor the requisite human-detected keyframes. Thus, the experimental results of HOG-SVM illustrate a compression ratio of 98.54%, which is preferable to the proposed work's compression ratio of 98.71%, which supports the criminal investigation.

show abstract

Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network

Cited by 27 publications

References 9 publications

Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

Fine Grained Feature Extraction Model of Riot-related Images Based on YOLOv5

SCDet: A Robust Approach for the Detection of Skin Lesions

An Enhanced Spatio-Temporal Human Detected Keyframe Extraction

Contact Info

Product

Resources

About