A YOLOv3-Based Smart City Application For Children’s Playgrounds

Abstract

According to the reports of Public Health Institution, approximately 250,000 rabies-risk animal bites occur per year in Turkey. Most of these bites are caused by dogs and most of the victims are the children who play in playgrounds. With the development of deep learning-based computer vision technology, autonomous detection of dangerous objects (handguns, knives, dogs, etc.) in these children’s playgrounds has become a crucial security application. In this paper, a real-time dog detection model based on YOLOv3 deep learning algorithm is proposed as a new smart city security application and this model is applied to the selected children’s playground. Firstly, in view of the problem of insufficient stray dog image data in the original datasets, new images of stray dogs have been taken from an animal shelter and they have been added to the dataset. These new images have effectively enriched the diversity of training data and improved the training performance of the proposed model. The proposed model has been optimized by utilizing various hyperparameters and the results have been compared with each other. The model with the best evaluation scores is proposed and applied to detect dogs automatically by the fast emergency station located in the selected children’s playground. The real-time application has achieved 82.59% of AP with adjusted hyperparameters.