Development of Face Mask Detection using SSDLite MobilenetV3 Small on Raspberry Pi 4
Nenny Anggraini, Syarif Hilmi Ramadhani, Luh Kesuma Wardhani, Nashrul Hakiem, Imam Marzuki Shofi, M. Tabah Rosyadi
Abstract
This study aimed to develop a mask detection tool with SSDLite MobilenetV3 Small based on Raspberry Pi 4. SSDLite MobilenetV3 Small is a single-stage object detection. The single-stage object detection method is faster than the two-stage detection method. However, it has the disadvantage as the level of accuracy is not as good as the two-stage detection method. In the experiments, we used some methods to compare with SSDLite MobilenetV3, such as: SSDLite MobilenetV3 Large, SSDLite MobilenetV2, SSD MobilenetV2, SSDLite Mobileedets, and SSDMNV2 models. The result is that SSDLite MobilenetV3 is more powerful than other systems for detecting face masks. While the model with the best detection is the SSDLite MobilenetV2 model, the system with the SSDLite MobilenetV3 Small model still detects the use of masks, with a score of 70% accuracy from model accuracy testing in deployment. The limitation is the system with SSDLite MobilenetV3 Small can't detect incorrect masks.