The existing algorithms for detecting dress code violations at airports exhibit high computational complexity and weak real-time performance. Furthermore, they are prone to errors and omissions during detection in complex airport security scenarios, making it difficult to meet the requirements of real-time security detection. In response to this situation, a method called SGS-YOLO is proposed based on the YOLOv8n technology route for detecting violations of dress code by airport security personnel. First, a parameter-free SimAM attention mechanism is introduced into the backbone network of the model to enhance the perception ability of important features and improve the accuracy of object detection. Second, GSConv and VoV-GSCSP modules are introduced into the neck network to reduce the number of parameters, which helps achieve lightweighting of the model. Finally, a detection box regression loss function based on SIOU is adopted to reduce misjudgments in cases involving small changes between the predicted and real target boxes. The experimental results show that compared with the baseline model, the SGS-YOLO improves the average accuracy by 6.3 percentage points. Further, it reduces the number of parameters and floating-point operations by 9.63% and 8.64%, respectively. The proposed approach effectively achieves a balance between model lightweighting and performance, and thus, it possess good engineering application value.