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Smart and Explainable Analysis of University Dress Code Compliance using Computer Vision: A Hybrid Approach Combining YOLOv8, EfficientNet-B0, and Grad-CAM

by Donatien Kadima Muamba, Bernice Mambula Zuiya, Jean-Jacques Kabasubabo Kalombo, Hénoc Kabengela Umira
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 140
Year of Publication: 2026
Authors: Donatien Kadima Muamba, Bernice Mambula Zuiya, Jean-Jacques Kabasubabo Kalombo, Hénoc Kabengela Umira
10.5120/ijcae554156110f2

Donatien Kadima Muamba, Bernice Mambula Zuiya, Jean-Jacques Kabasubabo Kalombo, Hénoc Kabengela Umira . Smart and Explainable Analysis of University Dress Code Compliance using Computer Vision: A Hybrid Approach Combining YOLOv8, EfficientNet-B0, and Grad-CAM. International Journal of Computer Applications. 187, 140 ( Sep 2026), 64-73. DOI=10.5120/ijcae554156110f2

@article{ 10.5120/ijcae554156110f2,
author = { Donatien Kadima Muamba, Bernice Mambula Zuiya, Jean-Jacques Kabasubabo Kalombo, Hénoc Kabengela Umira },
title = { Smart and Explainable Analysis of University Dress Code Compliance using Computer Vision: A Hybrid Approach Combining YOLOv8, EfficientNet-B0, and Grad-CAM },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2026 },
volume = { 187 },
number = { 140 },
month = { Sep },
year = { 2026 },
issn = { 0975-8887 },
pages = { 64-73 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number140/smart-and-explainable-analysis-of-university-dress-code-compliance-using-computer-vision-a-hybrid-approach-combining-yolov8-efficientnet-b0-and-grad-cam/ },
doi = { 10.5120/ijcae554156110f2 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-09-19T02:57:08.824888+05:30
%A Donatien Kadima Muamba
%A Bernice Mambula Zuiya
%A Jean-Jacques Kabasubabo Kalombo
%A Hénoc Kabengela Umira
%T Smart and Explainable Analysis of University Dress Code Compliance using Computer Vision: A Hybrid Approach Combining YOLOv8, EfficientNet-B0, and Grad-CAM
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 140
%P 64-73
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The adherence to dress code regulations in universities and higher education institutions is an institutional concern that may require more effective, consistent and transparent monitoring approaches. In this paper, we describe the design and implementation of VestiAI, an explainable artificial intelligence system for automated university dress-code compliance assessment. The proposed hybrid architecture consists of three complementary components : YOLOv8 to detect and localize the clothing items in the images, EfficientNet-B0 to classify the attire in two categories (compliant and non-compliant), and Grad-CAM to explain the visual regions that contribute to the classification decisions. Compared to conventional methods that only offer a final classification result, the presented framework integrates automated assessment with visual explanations, thus improving the interpretability of the decision-making process. The dataset contains 1,337 images which are divided into train, validation and test sets. For clothing detection, YOLOv8 achieved an overall precision of 81.09%, recall of 83.97%, mAP@50 of 86.51%, and mAP@50-95 of 59.27%. For compliance classification, the accuracy, precision, sensitivity and F1-score of EfficientNet-B0 was 81.77%, 79.65%, 86.54% and 82.95% respectively on the 203-image test set. The class-wise detection results also showed the performance variations between the clothing categories, with the best performance on pants with a mAP@50 of 93.99% and the more challenging shoes and hats with the more strict mAP@50-95. Then a web application based on FastAPI was developed to provide a practical interface for image submission and for automatic assessment. The system generates a report detailing the detected garments, the estimated compliance status, and Grad-CAM visualizations that highlight the regions that influenced the classification decision. Results demonstrate the viability of integrating object detection, deep image classification, and explainable artificial intelligence for automatic university dress-code evaluation. The proposed framework aims to support decision making and human validation, not to replace human judgment.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence computer vision dress code compliance YOLOv8 EfficientNet-B0 Grad-CAM explainability Deep Learning academic environment.

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