Publication: Vision-based human violence detection using yolov8 object detection algorithm
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering::Electrical and electronic engineering | |
| dc.contributor.author | Muhammad Arsyad bin Rahman Yusof | |
| dc.date.accessioned | 2025-05-27T07:33:06Z | |
| dc.date.available | 2025-05-27T07:33:06Z | |
| dc.date.issued | 2024-07 | |
| dc.description.abstract | Despite advancements in violence detection technologies, existing models face challenges in real-time processing and generalization across diverse scenarios, necessitating the development of a more efficient and accurate system. The primary objective is to develop an efficient deep learning model for real-time violence detection using the YOLOv8 object detection framework, thereby improving the effectiveness and reliability of surveillance systems in diverse and dynamic real-world environments. The methodology involves compiling a diverse dataset of violent and non-violent interactions from multiple online repositories, employing data augmentation techniques, and training the YOLOv8 model using transfer learning. The YOLOv8 model achieved a precision of 80.7% recall of 74.2%, an F1 score of 77.42%, and accuracy of 82.5% demonstrating superior performance in detecting violent and able to process in real-time. The model's robustness and generalization capabilities were validated across various test scenarios. This research successfully developed a effective and efficient vision-based violence detection system using the YOLOv8 model, offering significant potential for application in real-world security and surveillance systems. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/21928 | |
| dc.language.iso | en | |
| dc.title | Vision-based human violence detection using yolov8 object detection algorithm | |
| dc.type | Resource Types::text::report::technical report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |