YOLO-BASED VISUAL INSPECTION IN INDUSTRY 4.0: A COMPREHENSIVE REVIEW OF SMART MANUFACTURING APPLICATIONS

Abstract

The transition toward Industry 4.0 has intensified the demand for zero-defect manufacturing, making Automated Visual Inspection a critical component of smart factories. However, implementing deep learning in real-world production faces significant challenges, particularly regarding data scarcity and edge deployment constraints. This paper provides a comprehensive review of YOLO-based applications in smart manufacturing, categorizing recent implementations across surface defect detection, assembly verification, robotic vision, and predictive maintenance. Our analysis reveals a critical paradigm shift from model-centric optimization toward data-centric strategies. While advanced architectures improve detection precision, integrating few-shot learning techniques and lightweight models is essential to overcome industrial data limitations. Ultimately, this review establishes a foundational roadmap for achieving vision-driven, zero-defect production in resource-constrained factory environments.