Deploying Deep Learning for Facemask Detection in Mobile Healthcare Units
Аннотация
Identifying facemasks is an important duty that affects public health and safety, especially during epidemics of communicable diseases. Many architectures of deep learning models are being investigated for their effectiveness, as they have demonstrated great potential in automating this process. The performance of four well-known deep learning architectures—VGG19, VGG16, GRU, and Fully Convolutional Neural Networks (FCNN)—for facemask identification is thoroughly compared in this thesis. The goal of the study is to assess these architectures in terms of accuracy, efficiency, and robustness in order to offer important information for the creation of efficient facemask detection systems.
This study examines the advantages and disadvantages of each model in relation to facemask detection through thorough testing and analysis. The models are statistically evaluated for their ability to detect facemasks in pictures or video streams using performance metrics including precision, recall, and F1-score. Furthermore, the actual feasibility of using these models in real-world applications is assessed by analyzing computational efficiency measures like inference time and model size.
Moreover, the models' resilience is assessed in a range of demanding scenarios, such as changes in illumination, facial expressions, and occlusions. The consequences of these results are discussed in the thesis along with suggestions for improving each architecture for facemask detection tasks.
This dissertation advances facemask detection technology by providing a thorough comparative examination of these deep learning architectures. Additionally, it offers helpful recommendations for researchers and practitioners in the computer vision and public health fields.
This study examines the advantages and disadvantages of each model in relation to facemask detection through thorough testing and analysis. The models are statistically evaluated for their ability to detect facemasks in pictures or video streams using performance metrics including precision, recall, and F1-score. Furthermore, the actual feasibility of using these models in real-world applications is assessed by analyzing computational efficiency measures like inference time and model size.
Moreover, the models' resilience is assessed in a range of demanding scenarios, such as changes in illumination, facial expressions, and occlusions. The consequences of these results are discussed in the thesis along with suggestions for improving each architecture for facemask detection tasks.
This dissertation advances facemask detection technology by providing a thorough comparative examination of these deep learning architectures. Additionally, it offers helpful recommendations for researchers and practitioners in the computer vision and public health fields.