Mask Please!
Real-time face mask detection
Implementing transfer learning in computer vision to enhance image recognition models by leveraging pre-trained neural networks for improved accuracy and efficiency.
Client
RV College of Engineering
Services
Computer Vision
Industries
Widely applicable
Date
November 2022
In researching real-time face-mask detection using computer vision, I explored existing literature on mask detection models and identified the MobileNet SSD model as a suitable pre-trained option. Leveraging TensorFlow Model Zoo, I tailored the model for live camera feed applications. The utilization of numpy, cv2, and tensorflow libraries facilitated seamless integration into the system.
The solution successfully decreased human intervention in mask compliance checks, ensuring a more efficient and contactless process. This not only enhanced safety protocols but also demonstrated the effectiveness of transfer learning in rapidly deploying robust computer vision applications for real-world scenarios.