Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety
Abstract In today’s digital environment, effectively detecting and censoring harmful and offensive objects such as weapons, addictive substances, and violent content on online platforms is increasingly important for user safety. This study introduces an Enhanced Object Detection (EOD) model that bui...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08413-4 |
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| author | Mohammed Kawser Jahan Fokrul Islam Bhuiyan Al Amin M. F. Mridha Mejdl Safran Sultan Alfarhood Dunren Che |
| author_facet | Mohammed Kawser Jahan Fokrul Islam Bhuiyan Al Amin M. F. Mridha Mejdl Safran Sultan Alfarhood Dunren Che |
| author_sort | Mohammed Kawser Jahan |
| collection | DOAJ |
| description | Abstract In today’s digital environment, effectively detecting and censoring harmful and offensive objects such as weapons, addictive substances, and violent content on online platforms is increasingly important for user safety. This study introduces an Enhanced Object Detection (EOD) model that builds upon the YOLOv8-m architecture to improve the identification of such harmful objects in complex scenarios. Our key contributions include enhancing the cross-stage partial fusion blocks and incorporating three additional convolutional blocks into the model head, leading to better feature extraction and detection capabilities. Utilizing a public dataset covering six categories of harmful objects, our EOD model achieves superior performance with precision, recall, and mAP50 scores of 0.88, 0.89, and 0.92 on standard test data, and 0.84, 0.74, and 0.82 on challenging test cases–surpassing existing deep learning approaches. Furthermore, we employ explainable AI techniques to validate the model’s confidence and decision-making process. These advancements not only enhance detection accuracy but also set a new benchmark for harmful object detection, significantly contributing to the safety measures across various online platforms. |
| format | Article |
| id | doaj-art-1c7fa093996b449da3689a669efb50fa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1c7fa093996b449da3689a669efb50fa2025-08-20T03:37:30ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-08413-4Enhancing the YOLOv8 model for realtime object detection to ensure online platform safetyMohammed Kawser Jahan0Fokrul Islam Bhuiyan1Al Amin2M. F. Mridha3Mejdl Safran4Sultan Alfarhood5Dunren Che6Department of Computer Science, American International University-BangladeshDepartment of Computer Science, American International University-BangladeshDepartment of Computer Science, American International University-BangladeshDepartment of Computer Science, American International University-BangladeshResearch Chair of Online Dialogue and Cultural Communication, Department of Computer Science, College of Computer and Information Sciences, King Saud UniversityResearch Chair of Online Dialogue and Cultural Communication, Department of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Electrical Engineering and Computer Science, Texas A&M University-KingsvilleAbstract In today’s digital environment, effectively detecting and censoring harmful and offensive objects such as weapons, addictive substances, and violent content on online platforms is increasingly important for user safety. This study introduces an Enhanced Object Detection (EOD) model that builds upon the YOLOv8-m architecture to improve the identification of such harmful objects in complex scenarios. Our key contributions include enhancing the cross-stage partial fusion blocks and incorporating three additional convolutional blocks into the model head, leading to better feature extraction and detection capabilities. Utilizing a public dataset covering six categories of harmful objects, our EOD model achieves superior performance with precision, recall, and mAP50 scores of 0.88, 0.89, and 0.92 on standard test data, and 0.84, 0.74, and 0.82 on challenging test cases–surpassing existing deep learning approaches. Furthermore, we employ explainable AI techniques to validate the model’s confidence and decision-making process. These advancements not only enhance detection accuracy but also set a new benchmark for harmful object detection, significantly contributing to the safety measures across various online platforms.https://doi.org/10.1038/s41598-025-08413-4Harmful object detectionVisual censorship systemsGraphic violenceInsulting gestureYOLOv8 |
| spellingShingle | Mohammed Kawser Jahan Fokrul Islam Bhuiyan Al Amin M. F. Mridha Mejdl Safran Sultan Alfarhood Dunren Che Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety Scientific Reports Harmful object detection Visual censorship systems Graphic violence Insulting gesture YOLOv8 |
| title | Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety |
| title_full | Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety |
| title_fullStr | Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety |
| title_full_unstemmed | Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety |
| title_short | Enhancing the YOLOv8 model for realtime object detection to ensure online platform safety |
| title_sort | enhancing the yolov8 model for realtime object detection to ensure online platform safety |
| topic | Harmful object detection Visual censorship systems Graphic violence Insulting gesture YOLOv8 |
| url | https://doi.org/10.1038/s41598-025-08413-4 |
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