Deep learning-based car seatbelt classifier resilient to weather conditions

Abstract

Deep Learning is a very promising field in image classification. It leads to the automation of many real-world problems. Currently, Car seatbelt violation detection is done manually or partial manual. In this paper, an approach is proposed to make the seat belt detection process fully automated. To make the detection more accurate, sensors are set to detect the weather condition. When spe-cific weather condition is detected, the corresponding pre-trained model is assigned the detection task. In other words, a research is conducted to check the possibility of dividing the big-sized deep-learning model - that can classify car seatbelt, into sub-models each one can detect specific weather condition. Accordingly, a single specialized model is used for each weather condition, Deep convolutional neural network (CNN) model AlexNet is used in the detection/classification process. The proposed system is sensor based AlexNet (S-AlexNet). Results support our hypothesis that “Using single model for each weather condition is better than gen-eral model that support all weather conditions”. On average, previous approaches that trained single model for all weather condi-tions have accuracy less than 90%. The proposed S-AlexNet approach successfully reaches 90+% accuracy.