Detection of Violent Scenes in Cartoon Movies Using a Deep Learning Approach

Cartoon movies are a primary source of entertainment for children. However, concerns arise when these movies inadvertently expose children to violent scenes. This paper addresses the challenge of detecting subtle instances of violence within cartoon movies. The main difficulty in this task is the sequential nature of movies, where a sequence of frames must be considered in their given order. Existing methods have not effectively addressed the issue. In this study, we tackled this challenge by employing a sequential model. The research comprises three key steps. Initially, a histogram technique was implemented to select key frames from the video sequences. Subsequently, a Convolutional Neural Network (CNN) was utilized to extract prominent features from these selected key frames. In the third phase, the acquired features were utilized to train a sequential model using sequence-based learning. The model was then refined through transfer learning, using a dataset containing scenes devoid of violence, as well as scenes depicting varying forms of violence, including bloodshed, fights, gunshots, and explosions. A significant contribution of this study is the meticulous categorization of violent scenes into four distinct types, allowing for further investigation into the diverse effects of different violence categories. Furthermore, the study introduces an innovative approach by integrating a dense layer into the sequential model to enhance final classification. The trained model's performance was comprehensively evaluated using metrics such as F1 score, precision, accuracy, and recall. To validate the effectiveness of the proposed model, it was benchmarked against state-of-the-art methods. This study presents an innovative deep-learning methodology for the identification of violent scenes in cartoon movies. Its potential applications encompass a wide range, including safeguarding children from inappropriate content.