Published Paper Details:

Maintaining discipline and security in educational institutions requires ensuring adherence to standard regulations. YOLOv11, OpenCV, and dlib are used in this study's AI-driven real-time Uniform Detection System to automatically identify and categorise people according to their uniform status. The device uses face recognition to identify pupils and confirm that they are wearing the necessary uniform. It also incorporates a video capture camera for ongoing surveillance. An automatic email notice is sent to the relevant authorities and pupils in the event that a uniform violation is found. A unique YOLOv11 dataset is used to train the algorithm, guaranteeing precise distinction between uniforms and casual attire. The device also flags any security issues by identifying unauthorised individuals. The system saves captured frames of infractions for later study and keeps detection logs with timestamps, uniform status, and facial recognition results to improve data management. Notifications are tracked via logging email alerts. OpenCV is used for real-time image processing, Roboflow is used for dataset preparation, and Google Colab is used for model training. This automated method guarantees adherence to institutional policies, improves security, and decreases the need for manual oversight. IoT-based remote alerts and dataset extension are examples of future advancements that will increase accuracy and dependability.