The Novel of Using Transfer Learning Approach for Seatbelts Automated Surveillance

Abstract

The number of cars on the road has increased significantly as a result of the development of society, and this is one of the problems that traffic officials have focused on when diagnosing seat belts. With the increase in traffic accidents in recent years due to drivers not adhering to safety rules, it has become necessary to focus on this area. Seat belt diagnosis is an important rule that must be followed in the field of deep learning. In this paper, transfer learning is applied in seat belt diagnosis to reduce the number of risks and to protect passengers and drivers from traffic accidents when the seat belt is not used. The Xception model is proposed because this model has very deep hidden layers which leads to good metrics, the model is trained on the ImagNet dataset using fine-tuning learning. We find that previous training on ImageNet leads to a significant increase in the efficiency of the proposed architecture and extracts the important feature in a variety of situations to determine whether the driver has fastened his seat belt or not. The results show that the model can inspect seat belts with a high accuracy of 99.42% and a loss function of 8.15%.