Web-based Safety Eyewear Detection System in Workplace Using Machine Learning

Artificial Intelligence (AI) and computer vision have provided various ways to solve problems in our daily life. In this paper, a web-based safety eyewear detection system to detect the presence of safety eyewear in input images or video streams is developed using OpenCV, TensorFlow/Keras, and deep learning. This detection system is necessary to be deployed at risky workplaces such as construction sites to help reduce the risks of accidents and facilitate supervisors to detect workers who do not adhere to the regulations of wearing safety eyewear before entering a construction site. This paper uses a combination of transfer learning techniques using a pre-trained MobileNet architecture and Single Shot Detection framework to build a fast and efficient deep learning-based method for safety eyewear detection. With the help of Streamlit, the model is deployed into a web application to provide a user-friendly interface for the users. This web application can detect faces instantly by applying the safety eyewear classifier efficiently and quickly. Experimental results on the dataset collected demonstrated the superior performance of the proposed model with 98% accuracy.