Athirah Mohammad Alsughayyir and Ala Saleh Alluhaidan, Saudi Arabia

The use of automated technology has the potential to enhance road safety and prevent fatalities. A new approach for detecting the presence of a child in a car's front seat using image processing techniques can prevent accidents caused by children being in the front seat. By analyzing images from a surveillance camera, we can identify the location and analyze pixel values using machine learning algorithms to determine if a child is present in the image. This technology can help prevent severe accidents caused by leaving children in the front seat of a car. Our goal is to develop an AI-based deep learning algorithm that can detect children in the front seat of a car by analyzing various features of the child and classifying them as either a child or adult. This algorithm will also be used to detect the presence of people in the car and monitor the street, automatically sending traffic violation information to the driver. Our training dataset had 2,624 images, the validation dataset had 254 images, and the testing dataset had 316 images. We achieved a training accuracy of 97%, a validation accuracy of 95.67%, and a testing accuracy of 86%. The classification report is displayed in the figure provided.

Children detection, front seat, image processing, car, deep learning.