# Violence-YOLO: Enhanced GELAN Algorithm for Violence Detection
## 4 Experimental Design and Analysis of Results
### Comparative Experiments  
To further validate the effectiveness of the proposed model, we trained various target detection models of similar size using the same test set and configuration conditions. Compared with classical algorithms such as YOLOv9-C and RT-DETR, our model demonstrates higher accuracy in detecting violent behaviors. As shown in Table 4, the [EMAIL_ADDRESS] and mAP@[0.5:0.95] of the proposed Violence-YOLO algorithm reach 92.6% and 75.0%, respectively. The number of parameters is slightly higher than that of YOLOv3-tiny, but the GFLOPs are relatively low. Although the parameters and computational requirements of the YOLOv3-tiny algorithm are lower than those of our model, its detection accuracy is significantly lower. In addition, although the computational requirements of the YOLOv9-C and RT-DETR algorithms are much higher, their average detection accuracies are still inferior to ours. Compared with all aforementioned algorithms, the proposed Violence-YOLO violence detection model excels in detection performance while maintaining lower parameters and computational effort. This demonstrates the feasibility and effectiveness of the improved algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **mAP50/\%** & **mAP50-95/\%** & **Parameters/M** & **FLOPs/G** & **FPS** \\ \hline GD-GELAN & 91.9 & 74.6 & 22.1 & 88.9 & 48.5 \\ +SimAM & 91.9 & 73.9 & 22.1 & 88.9 & 49.7 \\ \hline +SKA & 92.3 & 75.2 & 44.2 & 159.5 & 37.3 \\ \hline +FocalModulation & 91.0 & 73.0 & 23.2 & 92.9 & 46.3 \\ \hline +SE & 91.8 & 75 & 22.1 & 88.9 & 49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiments with different attentional mechanisms on Violence data set.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Baseline** &
\begin{tabular}{c} **Ghost** \\ **Lightweight** **Modules** \\ \end{tabular} & **Dysample** & **SimAM** & **Focaler-IoU** & **mAP50/\%** & **mAP50-95/\%** & **Parameters/M** & **FLOPs/G** & **FPS** \\ \hline \hline \multirow{4}{*}{GELAN-C} & ✓ & & & 91.7 & 74.9 & 25.2 & 101.8 & 47.6 \\  & ✓ & & & 91.6 & 74.2 & 22.1 & 88.9 & 48.0 \\ \cline{1-1}  & ✓ & ✓ & & 91.8 & 74.3 & 22.1 & 88.9 & 48.0 \\ \cline{1-1}  & ✓ & ✓ & ✓ & 91.9 & 74.6 & 22.1 & 88.9 & 48.5 \\ \cline{1-1}  & ✓ & ✓ & ✓ & ✓ & 92.6 & 75 & 22.1 & 88.9 & 47.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments with the modules on Violence data set. The checkmarks represent substitutions or overlays with our improvement modules on the GENLAN-C model.  
In order to verify the generalizability of our proposed method, we conducted experiments on the Pascal VOC dataset and compared its performance with that of GELAN-C and some other state-of-the-art detectors. The experimental results are shown in Table 5, where the performance metrics of the compared detectors are derived from their respective original articles. The results show that while YOLOv8m-World accuracy and detection speed are better than that of our method, it is not as good as our method in terms of the number of model parameters, computational complexity, and mean accuracy (mAP). YOLOv5m is also better than our method only in terms of detection speed and computational complexity. Compared to GELAN-C, with a loss of 0.3% of mean accuracy (mAP50), the precision (P) is improved by 1%, the computational load is reduced by 12.7%, the model size is reduced by 12%, and the detection speed is slightly improved. Taken together, our proposed model works better, which proves its generalizability on public datasets.