Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19
This work addresses social distancing enforcement for public health during the pandemic, but it is incremental as it modifies an existing YOLOv5 architecture with minor gains.
The paper tackles the problem of monitoring social distancing during Covid-19 by developing a YOLOv5-based system using an overhead view, achieving an accuracy of 81.7% on the COCO dataset with a modified model, which is a 1.6% improvement over the default model.
The purpose of this work is, to provide a YOLOv5 deep learning-based social distance monitoring framework using an overhead view perspective. In addition, we have developed a custom defined model YOLOv5 modified CSP (Cross Stage Partial Network) and assessed the performance on COCO and Visdrone dataset with and without transfer learning. Our findings show that the developed model successfully identifies the individual who violates the social distances. The accuracy of 81.7% for the modified bottleneck CSP without transfer learning is observed on COCO dataset after training the model for 300 epochs whereas for the same epochs, the default YOLOv5 model is attaining 80.1% accuracy with transfer learning. This shows an improvement in accuracy by our modified bottleneck CSP model. For the Visdrone dataset, we are able to achieve an accuracy of upto 56.5% for certain classes and especially an accuracy of 40% for people and pedestrians with transfer learning using the default YOLOv5s model for 30 epochs. While the modified bottleneck CSP is able to perform slightly better than the default model with an accuracy score of upto 58.1% for certain classes and an accuracy of ~40.4% for people and pedestrians.