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Deep labeller : automatic bounding box generation for synthetic violence detection datasets
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Nadeem, Muhammad Shahroz, Kurugollu, Fatih, Saravi, Sara, Atlam, Hany F. and Franqueira, Virginia N. L. (2024) Deep labeller : automatic bounding box generation for synthetic violence detection datasets. Multimedia Tools and Applications, 83 . pp. 10717-10734. doi:10.1007/s11042-023-15621-5 ISSN 1380-7501.
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Official URL: http://dx.doi.org/10.1007/s11042-023-15621-5
Abstract
Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Data sets, Violence, Labels, Deep learning (Machine learning) | ||||||||
Journal or Publication Title: | Multimedia Tools and Applications | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 1380-7501 | ||||||||
Official Date: | January 2024 | ||||||||
Dates: |
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Volume: | 83 | ||||||||
Page Range: | pp. 10717-10734 | ||||||||
DOI: | 10.1007/s11042-023-15621-5 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 26 December 2023 | ||||||||
Date of first compliant Open Access: | 4 January 2024 |
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