A Framework for Dress Code Monitoring System using Transfer Learning from Pre-Trained YOLOv4 Model

Maintaining a proper dress code in organizations or any environment is very important. It not only imbibes a sense of discipline but also reflects the personality and qualities of people as individuals. To follow this practice, some organizations like educational institutions and a few corporations have made it mandatory for the personnel to maintain proper attire as per their regulations. Manual checks are performed to adhere to the organizations’ regulations which becomes tedious and erroneous most of the times. Having an automated system not only saves time but also there is very little scope of mistakes and errors. Taking this into context, the main aim and idea behind the project is to propose a model for detecting the dress code in such workplaces and educational institutions where the attire needs to be regularly monitored. The model detects Business Formals (Blazer, Shirt & Pants) worn by the personnel, for which CNN has been considered, along with YOLOv4, for performing the detection, due to its nature of giving the highest accuracy in comparison to the other object-detection models. Providing the Mean Average Precision of around 81%, it becomes evident that the model performs quite well in performing the detections.