Violence-YOLO: Enhanced GELAN Algorithm for Violence Detection DOI Creative Commons
Wenbin Xu,

Dingju Zhu,

Renfeng Deng

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(15), С. 6712 - 6712

Опубликована: Авг. 1, 2024

Violence is a serious threat to societal health; preventing violence in airports, airplanes, and spacecraft crucial. This study proposes the Violence-YOLO model detect accurately real time complex environments, enhancing public safety. The based on YOLOv9’s Generalized Efficient Layer Aggregation Network (GELAN-C). A multilayer SimAM incorporated into GELAN’s neck identify attention regions scene. YOLOv9 modules are combined with RepGhostNet GhostNet. Two modules, RepNCSPELAN4_GB RepNCSPELAN4_RGB, innovatively proposed introduced. shallow convolution backbone replaced GhostConv, reducing computational complexity. Additionally, an ultra-lightweight upsampler, Dysample, introduced enhance performance reduce overhead. Finally, Focaler-IoU addresses neglect of simple difficult samples, improving training accuracy. datasets derived from RWF-2000 Hockey. Experimental results show that outperforms GELAN-C. [email protected] increases by 0.9%, load decreases 12.3%, size reduced 12.4%, which significant for embedded hardware such as Raspberry Pi. can be deployed monitor places effectively handling backgrounds ensuring accurate fast detection violent behavior. In addition, we achieved 84.4% mAP Pascal VOC dataset, reduction parameters compared previously refined detector. offers insights real-time behaviors environments.

Язык: Английский

DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model DOI Creative Commons
Fatma S. Alrayes, Hamed Alqahtani, Wahida Mansouri

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 16, 2025

Язык: Английский

Процитировано

0
Violence-YOLO: Enhanced GELAN Algorithm for Violence Detection DOI Creative Commons
Wenbin Xu,

Dingju Zhu,

Renfeng Deng

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(15), С. 6712 - 6712

Опубликована: Авг. 1, 2024

Violence is a serious threat to societal health; preventing violence in airports, airplanes, and spacecraft crucial. This study proposes the Violence-YOLO model detect accurately real time complex environments, enhancing public safety. The based on YOLOv9’s Generalized Efficient Layer Aggregation Network (GELAN-C). A multilayer SimAM incorporated into GELAN’s neck identify attention regions scene. YOLOv9 modules are combined with RepGhostNet GhostNet. Two modules, RepNCSPELAN4_GB RepNCSPELAN4_RGB, innovatively proposed introduced. shallow convolution backbone replaced GhostConv, reducing computational complexity. Additionally, an ultra-lightweight upsampler, Dysample, introduced enhance performance reduce overhead. Finally, Focaler-IoU addresses neglect of simple difficult samples, improving training accuracy. datasets derived from RWF-2000 Hockey. Experimental results show that outperforms GELAN-C. [email protected] increases by 0.9%, load decreases 12.3%, size reduced 12.4%, which significant for embedded hardware such as Raspberry Pi. can be deployed monitor places effectively handling backgrounds ensuring accurate fast detection violent behavior. In addition, we achieved 84.4% mAP Pascal VOC dataset, reduction parameters compared previously refined detector. offers insights real-time behaviors environments.

Язык: Английский

Процитировано

3