Development Of A Child Detection System With Artificial Intelligence (Ai) Using Object Detection Method

Lai, Suk Na (2018) Development Of A Child Detection System With Artificial Intelligence (Ai) Using Object Detection Method. [Final Year Project Report] (Unpublished)

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Abstract

The issue of children dying due to vehicular heatstroke has raised the public attention. The failure of current vehicular occupant detection devices to identify correctly the occupant as a child had triggered the idea of developing a child detection system using Artificial Intelligence (AI) technology. The usage of Convolutional Neural Network (CNN) had been recognised as an effective way to perform image classification. However, this approach required a significant number of images as training data and substantial time for model training in order to achieve desired results in accuracy. Due to the limitation of abundant dataset, transfer learning was used to accomplish the task. Modern convolutional object detector, SSD Mobilenet v1 trained on Microsoft Common Objects in Context (MS COCO) dataset was used as a starting point of the training process. MS COCO dataset that consisted of a total of 328k images were divided into 91 different categories including dog, person, kite and so on. The trained model was then retrained to classify adults and children instead of persons. At the end of the training, a real-time child detection system was established. The system was able to give different responses to the detection of a child and adult. The responses comprised of visual and audio outputs. Upon detection, a bounding box was drawn on a child or an adult face as visual output. At the same time, the system would trigger the speaker to speak out the statement “child is detected” for successful child detection whereas adult detection would result in the statement of “adult is detected”. Theoretically, the detection system could achieve an overall precision of 0.969. However, the experimental results obtained was able to match up to a precision of 0.883 that resulted in a small error of 8.88%.

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