Deep Convolutional Neural Network for Fabric Defect Detection to Promote Sustainable Textile Entrepreneurship and Economic Resilience in Nigeria

Abstract

Nigeria's textile SMEs face persistent quality control challenges from manual fabric inspection, resulting in high defect rates, 20–40% material waste and rejection, reduced competitiveness, limited exports, and heavy reliance on imports. These issues constrain sustainable entrepreneurship and economic resilience in key clusters like Aba and Onitsha. This study develops a deep convolutional neural network model using transfer learning with EfficientNet-B0 to enable automated multi-class fabric defect detection. Trained and evaluated on the Multi-Class Fabric Defect Detection Dataset (3077 images, nine defect classes), the model achieves 94.8% accuracy and a weighted F1-score of 0.949. Grad-CAM heatmaps ensure visual explainability by precisely highlighting defect regions. For real-world usability in resource-limited Nigerian SMEs, the model is packaged as an offline, standalone GUI desktop application built with Tkinter and distributed via PyInstaller.