# SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
## 4 Results and Validation
### Web Deployment Testing  
As long as the target vehicles and pedestrians can be successfully detected, the experiment can deploy the tracking algorithm for further tracking. The study used two commonly used tracking algorithms for experimentation and testing, that is, BoT-SORT and ByteTrack. Figure 11 shows the algorithm model's web deployment and testing workflow. The BoT-SORT algorithm is an improved version of the SORT algorithm, which enhances tracking  
Figure 10: SGST-YOLOv8 and baseline model YOLOv8 visualization results.  
Figure 9: Validation result chart.  
capabilities in complex scenarios by integrating appearance and motion information. Especially when targets are occluded or crossed, its deep learning appearance model, combined with a refined cost function and rapid motion model, can optimize data and enhance the accuracy and robustness of target identification. It is applicable in areas such as video surveillance and autonomous driving. The ByteTrack algorithm improves the detection and tracking of small targets and performs excellently in handling target occlusion and interactions. A low-confidence detection box processing mechanism effectively enhances the tracking capability of small and temporarily occluded targets.  
This study evaluated the models by obtaining the FPS from three different surveillance footage scenarios (Table 4). To accurately evaluate the operating efficiency of the model, multiple counts were performed and the median average method was used to determine the final result of each scenario. This method can better capture the performance of the model in different scenarios. During the evaluation process, the experiment selected three representative monitoring screen scenarios and recorded the FPS values for each scenario. These scenarios include complex outdoor environments, simple outdoor scenes, and static scenes without physical objects for monitoring. By conducting multiple counts for each scenario, the experiment was able to obtain more reliable results and eliminate the influence of occasional outliers on the evaluation outcomes. It can be seen from the results that in the static scene without physical objects, the FPS difference between the improved YOLOv8 model, other enhanced models, and the baseline YOLOv8 model is minimal, with detection efficiency remaining relatively consistent. When transitioning to a simple scenario, a certain decrease in the detection efficiency of the model can be observed. After median averaging, the FPS of the improved model was 2.6 FPS higher than the baseline model. However, when entering the complex outdoor scene, the model's ability to handle complexity began to weaken. Compared to the simple scene, the FPS generally dropped by around 50%. The improved SGST-YOLOv8 model that takes GSConv+Triplet Attention as its core has better performance than other models. This result is crucial for practical applications, as it indicates that the model can effectively detect targets even in real-world environments.  
Figure 11: Baseline model YOLOv8 and SGST-YLOV8 visualization results.