Enterprise AI Analysis

The core contribution is a novel YOLOv8-based AI application designed to objectively and consistently enforce school dress codes, addressing human bias and inconsistencies of manual checks. It integrates real-time object detection with user-friendly notifications and a GUI, making it a scalable solution for educational institutions. The study systematically evaluated user acceptance using the UTAUT model, providing empirical evidence of the system's high acceptability and potential for widespread adoption. Furthermore, the research identifies specific performance limitations related to nuanced clothing categories, offering clear directions for future improvements in dataset quality, model fine-tuning, and advanced ID verification mechanisms.

The system is built on YOLOv8 (Ultralytics) for real-time object detection, running on an Intel Core i5 12th Gen processor, NVIDIA GeForce RTX 3050 GPU (4GB VRAM), 16GB DDR4 RAM, and a 512GB SSD on Windows 10. It consistently achieves approximately 30 FPS. The architecture includes a Video Capture Module, Preprocessing Module, Detection Module, Post-Processing Module, User Interface Module, and Data Logging Module. The model was fine-tuned on a custom dataset of 23 clothing categories (200 images each) and a pre-made dataset for hair detection. Performance was evaluated at 25%, 50%, and 75% confidence levels using TP, FP, FN, Precision, Recall, and F1-Score metrics, highlighting strengths in common attire and areas for improvement in visually similar or occluded items.

Ethical protocols were central to this study, ensuring privacy and data protection through strict confidentiality, non-storage of personal identifiers, and secure data management. Informed consent and IRB approval were obtained. Bias and fairness were addressed by using a single trained annotator, evaluating performance metrics (including confidence intervals), and implementing safeguards for gender detection to prevent discriminatory outcomes. Participant risks were minimized, and false positive implications were considered, with outputs used solely for research. Transparency and conflict of interest were managed through open disclosure and community involvement to ensure cultural appropriateness and positive social impact.