# Violence-YOLO: Enhanced GELAN Algorithm for Violence Detection
## 1 Introduction  
With the continuous development of modern society, the frequency and scope of violent behavior have gradually increased, posing serious challenges to social security and public safety. An effective violence detection system is of great significance in the field of surveillance, as it can help detect and respond to potential violent events promptly, thereby reducing unnecessary injuries and property losses. Traditional violent behavior detection methods mainly rely on manual surveillance or simple rule-based determination, suffering from low recognition accuracy and efficiency. With the development of computer vision and artificial intelligence, intelligent video surveillance is gradually replacing traditional video surveillance. Using computer vision technology to automatically identify and locate human violence in images or videos, it can effectively monitor and control violent scenes [1, 2]. This technology can be applied to streets, factories, schools, shopping centers, and other public places. Behavior recognition is an important research direction in the field of computer vision, enabling the automatic understanding of specific behavior semantics by analyzing bodymovements and motions of individuals in a scene [3; 4]. Violence detection generally relies on deep learning models for training and inference, which effectively reduces the labor costs associated with violence detection and improves both accuracy and efficiency. Traditional methods, such as motion detection, background subtraction, and target tracking, have been widely used for violence detection in surveillance systems. However, these methods have limitations in terms of accuracy and robustness, especially in complex and chaotic environments [5; 6]. Recent studies have shown that deep learning-based models, such as recurrent neural networks (RNN) and convolutional neural networks (CNN), can significantly improve the accuracy and efficiency of vision-based violence detection systems [7; 8]. These models can process and analyze image and video data, extract complex features, and identify violent events in real time. Despite recent advances in violence detection, the use of deep learning-based methods shows great promise in improving efficiency and accuracy. However, these methods still face challenges such as high computational requirements, restricted viewpoints, varying illumination conditions, complex crowd scenes, and intensity variations. Additionally, the weaker feature representation capability of the models and poor model generalization remain significant limitations [3; 9; 10]. Considering the above challenges and limitations, we improve YOLOv9, which offers outstanding advantages in terms of lightweight design, capturing richer gradient flow information and providing better feature representation, and apply it to violent behavior target detection. The system will be invaluable. The main contributions are as follows:  
* The simple, parameter-free attention module (SimAM) is integrated into the neck structure of GELAN to identify attention regions in the scene. * By integrating GhostNet and RepGhostNet modules into the YOLOv9 network model, we introduce two new modules, RepNCSPELAN4_GB and RepNCSPELAN4_RGB, in the backbone and neck networks. Furthermore, the shallow ordinary convolution in the backbone network is replaced with GhostConv, reducing computational complexity. * A lightweight universal upsampling operator, DySample, is used to replace the traditional nearest neighbor interpolation upsampling module, minimizing the loss of feature information during the upsampling process. * Combined with Focaler-IoU loss, it mitigates the neglect of simple and difficult samples, focusing on different regression samples, thus improving training accuracy. * Violence-YOLO, a violent behavior detection algorithm based on YOLOv9, is proposed. On our customized dataset, the average precision of detection during training ([EMAIL_ADDRESS]) reaches 92.6%, reflecting an improvement of 0.9%.