Real-time Mask Detection with YOLOv3 and Ensemble Residual Networks

Abstract

The proposed system introduced an advanced face mask detections framework leveraging Adaptively YOLOv3 with Hybrid Atrous Convolution-depend Face Detector, optimized through an Ensembles residual Learning Network. This approach aims to enhance the accuracy level and efficacy of face mask detection in real-time situation. By integrating of the adaptive capability of YOLOv3 on the spatial benefits of atrous convolution defined, the system effectively captured varying of facial features and masks placements. The ensemble residual learning further refined detection by amalgamating of multiple learning models, by reducing false positives values and negatives. This hybrid model is meticulous optimized to maintaining of higher detection precision rate and recall, for ensuring robust performance even over diverse and complex conditions. The system’s efficacy will underscored by reduces the inference time and enhanced processing throughput rate, by making it highly suitable for large-scale deployment on public safety applications. By rigorously testing phase, the proposed system demonstrated significant improvements in detection accuracy rate, inference speed, and overall reliability, positioning it will be a pivotal tool in enforcing healthy safety protocols on various public and private settings.