This paper investigates the critical challenge of detecting prohibited items in student dormitories, and we proposed a new deep-learning model to detect prohibited items automatically. To address the lack of an existing dataset for this task, we developed a new dataset, PISD (Prohibited Items in Student Dormitories). Our model achieved competitive performance, with the lowest GFLOPS and inference time, the highest FPS, and strong results in terms of precision, and recall highlighting its efficiency and effectiveness. This demonstrates the model’s capability to reliably detect and classify prohibited items in student dormitory environments.