# SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance
## 1 Introduction  
In China, with the expansion of the number of students on campuses, campus security is becoming more and more important, which brings great challenges to the maintenance of students' safety and campus order. With the continuous development of deep learning, intelligent surveillance based on target recognition has gradually been introduced into campuses, providing new solutions for campus stability. However, current target detection algorithms available on the market tend to be bulky when deployed on the web, so the network structure of the deployment model needs to be optimized. Real-time target detection is one of the key technologies in campus surveillance systems. It can automatically identify and track various targets on the campus, such as individuals and vehicles, based on their recognition IDs. However, traditional target detection algorithms such as HOG [1], SIFT [2], and DDPM [3] face performance challenges in campus surveillance scenarios, including low real-time capability and high computing resource consumption. [PERSON] et al. proposed a two-step target detection algorithm by combining Haar and HOG features, which improved the performance of multi-vehicle target detection and tracking in complex urban environments. The results showed that compared to traditional methods, this algorithm achieves higher detection accuracy and time efficiency [4]. [PERSON] et al. conducted experiments by combining HOG and LTP features and weighted the features of the color map and depth map to provide richer visual information. The results showed that this method improved the accuracy and efficiency of pedestrian detection [5]. With the continuous development of target detection algorithms, such as the emergence of new algorithms such as YOLO, Faster R-CNN, SSD, and RetinaNet, remarkable breakthroughs and progress have been made in the field of target detection. Currently, target detection algorithms can generally be categorized into two main directions: two-stagedetection and one-stage detection. Two-stage detection algorithms consist of two main components: (1) candidate region extraction and target classification and (2) boundary box regression, such as Faster R-CNN, Cascade R-CNN, and Mask R-CNN. Faster R-CNN is a type of target detection model that introduces a region proposal network (RPN). This model can simultaneously generate target proposals and their corresponding class probabilities, thus improving detection accuracy [6]. Cascade R-CNN enhances the performance of target detection through a cascaded approach, progressively filtering out more accurate target bounding boxes using a cascaded structure [7]. Based on Faster R-CNN, Mask R-CNN adds support for object instance segmentation, which can predict the boundary box and pixel level mask of the object at the same time [8]. Two-stage detection algorithms perform well in terms of accuracy, but correspondingly sacrifice a certain amount of detection speed. [PERSON] discussed the advantages and disadvantages of one-stage detection and two-stage detection through various variants of the current YOLO detector [9]. She found that in terms of detection accuracy, two-stage detection is generally superior to one-stage detection. However, they are slightly inferior in inference speed. [PERSON] et al. combined the Adversarial Occlusion Network (AON) with the standard Faster R-CNN detection algorithm to detect complex underwater targets [10]. The results showed that joint training for target detection helps alleviate overfitting caused by fixed pre-generated data, providing a promising detection solution for underwater exploration. [PERSON] et al. integrated the bidirectional feature pyramid network (BiFPN) into the Cascade R-CNN to overcome errors and omissions in target occlusion and small target scenes [11].