A mask detection approach by the employment of machine learning (ML) methods includes utilization of computer vision mechanisms to detect whether the person in the video or image data wearing mask or not. Specifically in the domain of public health analysis for diseases such as Covid-19, our approach obtained huge concentration.

Under machine learning mask detection, we combine various techniques and derive the best project, thesis writing service will be done in the best way by our writing team. We provide full and complete assistance along with thesis statement as per your specifications.

We discussed about the processing steps for the development of ML framework for identifying mask.

  1. Describe the Scope:
  • Objective: By analyzing non-dynamic images or actual time video data, we identify the existence of mask on person’s face.
  • Success Criteria: In this, we obtained a specified accuracy, recall and precision value.
  1. Collection of Data:
  • Images of persons with and without masks are considered by us and our collected dataset must be distinct and consists of images with various light effects, dimensions and different masks.
  • Initially, we can make use of publicly available datasets such as Real-world Masked Face Dataset (RMFD) and Masked Faces (MAFA) datasets.
  1. Data Preprocessing and Augmentation:
  • The images are defined with bounding boxes around faces and the labels denotes about the existence of mask.
  • We preprocessed the images by following various procedures like resizing, normalizing etc.
  • To enlarge the dimension and variety of data, datasets are augmented by us by considering several processes such as scaling, rotating and flipping
  1. Selection of Model:
  • Pre-trained methods are utilized in our approach for feature extraction process like VGGNet, MobileNet or ResNet. For object identification, these methods are mostly employed by integrate with techniques including Faster R-CNN or SSD.
  • If we need actual time execution or implemented in edge devices, we must make use of lightweight techniques.

 Training of Model:

  • By utilizing our mask dataset, the pre-trained model is fine-tuned.
  • For model adaption to the mask identification task, we utilized transfer learning.
  • We must sure about the framework’s generalizability through the use of methods such as cross-validation.
  1. Model Evaluation:
  • In terms of various metrics such as precision, accuracy, confusion matrix and F1-Score, our framework is examined.
  • To interpret where the framework making errors and the reason behind that, we performed error analysis.
  1. Model Optimization:
  • We enhance the efficiency through the framework optimization by considering the evaluation findings.
  • Gathering of enormous data, hyperparameter tuning and augmentation of data are the several factors considered by us.
  1. Deployment:
  • In a production platform, we implement our framework.
  • We should sure about the framework’s optimization for time-delay, if we are intended for actual-time identification.
  • With a proper interface for end-users, our framework is combined into a service or application.
  1. Monitoring and Maintenance:
  • Decrease in framework’s efficiency is detected by us through the continuous tracking.
  • To manage our framework’s accuracy and effectiveness, we often reconstruct it by using new data.

Tools & Libraries:

  • Data Annotation: We employed LabelImg, and VGG Image Annotator (VIA).
  • ML Models: Various frameworks are utilized by us such as Keras, TensorFlow and PyTorch.
  • Pre-Trained Models: In our research, Torch vision frameworks, TensorFlow Object Detection API are used.
  • Deployment: For deployment in web services, we used Flask/Django and for cross-platform, ONNX is utilized. For edge devices we make use of TensorFlow Lite.

Ethical Considerations:

  • Check whether the confidentiality is considered as important or not and we should not save the unrequired images for a longer period of time.
  • We must clear about the intention of considering mask identification task and the utilization of surveillance.
  • The terms such as false positives and negatives and the protocols for these terms are considered by us.

We develop an efficient machine learning framework for mask identification by following the above procedural steps.  Our framework can also deploy in several platforms to assist us to follow health and security rules. Struggling with your conference paper contact us we will help you out to derive the correct results.

Machine Learning Mask Detection Ideas


Machine Learning Mask Detection Project Topics are listed below, as per your interest you can select any one or we develop as per your own demand. Our work stands as a unique example for your academic career we provide thesis writing domain ideas for scholars. Our professional writer will provide you thesis of their own ideas and not a prewritten one. Research Work will be kept confidential which is our main ethics.

  1. Study of the performance of machine learning algorithms for face mask detection
  2. Face mask detection in Realtime environment using machine learning based google cloud
  3. Machine Learning Techniques and Systems for Mask-Face Detection—Survey and a New OOD-Mask Approach
  4. Improved and Accurate Face Mask Detection Using Machine Learning in the Crowded Places
  5. Identification of face mask and social distancing using YOLO algorithm based on machine learning approach
  6. Object detection via gradient-based mask R-CNN using machine learning algorithms
  7. An Embedded Machine Learning System For Real-time Face Mask Detection And Human Temperature Measurement
  8. Comparative analysis of various machine and deep learning models for face mask detection using digital images
  9. Face Mask Detection and Social Distancing Using Machine Learning with Haar Cascade Algorithm
  10. Detecting face masks through embedded machine learning algorithms: A transfer learning approach for affordable microcontrollers
  11. Automatic temperature detection and sanitization with authorized entry using face mask detection
  12. Comparative Analysis of Machine Learning Algorithms for Face Mask Detection and Alerting System
  13. Detecting face masks through embedded machine learning algorithms: A transfer learning approach for affordable microcontrollers
  14. Automated Face Mask Detector Using Machine Learning: An Approach to Reduce Burden on Healthcare System
  15. Constructing a software tool for detecting face mask-wearing by machine learning
  16. The Most Efficient and Accurate Face Mask Detection in Crowded Area using Machine Learning Algorithm
  17. A Blockchain-Enabled Machine Learning Mask Detection method for Prevention of Pandemic Diseases
  18. Real-Time Face Mask Detection using Computer Vision and Machine Learning
  19. A Machine Learning Approach for Face Mask Detection System with AdamW Optimizer

Machine learning based human body temperature measurement and mask detection by thermal imaging

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