Traditional Solution to Identify College Uniform Using CNN: Review
The purpose of this paper is to develop an AI-managed system that automatically automatically enforce the enforcement of university dress code policies using a tool, teachable machine, a device for training machine learning models. The system uses camera-based technology to monitor students as they enter the premises, ensure compliance with the dress code of the institute without the need for manual supervision. AI analyzes students' dress in real time, recognizing whether the required items such as formal shirts, tie, jackets or ID cards are present. If a violation is detected, the system immediately sends a notification to the student, which specifies the missing items (s), as well as a photo of the student for accurate time and record-keeping. By automating the process, the system reduces the capacity for human error, prejudice and inconsistent enforcement that is often accompanied by manual checks. This ensures fairness and fairness in the monitoring process, as AI depends only on visual data to assess compliance. Additionally, the system continuously operates, providing 24/7 surveillance without the requirement of brakes or supervision shifts. The AI-manufactured approach also increases efficiency by reducing the required time and effort from employees to manually check each student's dress. Real -time notifications enable students to fix any violations immediately, reduce the classroom disruption and improve the overall learning environment. With the required minimum human intervention after the setup, this automated system ensures a transparent, consistent and reliable method to maintain dress code compliance to benefit both students and university employees.
Authors
- Sujal Gajbhiye
- Md. Raufik Thekiya
- Bhushan Rajani
- Yash Mahadik
- Gunjar Ninawe
- Ritesh Shivankar
- Vaibhav Dhone
Publication Details
- Journal
- INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
- Published
- 2025-03-29
- DOI
- https://doi.org/10.55041/ijsrem43196
- Primary Topic
- Smart Systems and Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00