A Deep Learning Based Novel Approach for Detection of Face Mask Wearing using Enhanced Single Shot Detector (SSD) over Convolutional Neural Network (CNN) with Improved Accuracy

Abstract

The objective is to build an efficient face mask detector using Single Shot Detector (SSD). The algorithm used for face mask detection was a novel SSD and with the comparison of Convolutional Neural Network (CNN). The face mask detection dataset was usedand the ability of the algorithm was measured with the sample size of 136. SSD has achieved accuracy of 92.25% and for CNN it was 82.6%. By using a base architecture of VGG-16, SSD was able to outperform other object detectors like CNN without compromising speed and accuracy. The SSD and CNN are statistically satisfied with the independent sample t-test value (p<0.05) with a confidence level of 95%. Face mask detection using SSD was significantly better accurate than CNN.