Development of a Child Detection System with Artificial Intelligence Using Object Detection Method

Abstract

The issue of children dying due to vehicular heatstroke has raised significant concerns of public interest. The advancement of artificial intelligence (AI) technology, particularly in image classification and object detection, could be applied to overcome the current flaws of the vehicular occupant detection devices that often failed to serve as a triggering system to caretakers. In this paper, a technique for child detection with transfer learning is proposed. A real-time child detection system that consisted of a camera as an input medium, a classifier to detect the presence of a child and a triggering system in audio and visual forms was established. The modern convolutional object detector, SSD Mobilenet v1 was trained with Microsoft Common Objects in Context (MS COCO) dataset as a starting point of the training process. The model was then assessed and retrained to possess the ability to classify human into an adult or a child. The accuracy of the model was measured by counting the percentage of pixels labelled correctly per class. Based on the mean Average Precision (mAP), the detection system achieved an overall precision of 0.969 and the experimental results obtained showed a precision of 0.883, giving an error of less than ten percent.