Recent Advancements in Machine Vision Systems for Industrial Defect Detection: A Review

Abstract

Manufacturing, technology, and society experienced a number of changes throughout the Industrial Revolution. This paper presents a comprehensive review of advancements in Machine Vision Systems (MVS) for smart manufacturing and industrial quality control inspections, drawing from a range of studies that highlight advancements in optical systems, image acquisition techniques, and the pivotal role of deep learning methodologies. With the advent of AI and deep learning, MVS have achieved unprecedented levels of accuracy, speed, and versatility. It addresses key components of visual inspection systems, including optical illumination, image acquisition, and image processing, examining their impact on detection accuracy, efficiency, and robustness. The review identifies prevalent methods and future trends, offering insights for researchers and practitioners in manufacturing, computer vision, and quality control. Ultimately, this review aims to present the recent trends in the field of machine vision and image processing for defect detection in industrial applications, along with highlighting the research gaps for future work. Manufacturing processes inherently transform raw materials into finished products through a series of intricate operations that significantly impact the production line's overall efficiency and output quality. Defects and inconsistencies can lead to substantial economic losses, reputational damage, and potential safety hazards. The integration of machine vision systems has significantly enhanced defect detection in industrial manufacturing by improving efficiency, quality, and reliability. MVS have wide applications across industries, namely, automotive industry, electronics manufacturing, food and beverage industry, pharmaceutical industry and textile industry. Despite the numerous benefits of MVS, several challenges exist related to technical constraints, data requirements, adaptability and generalisation, and computational resources. The review concluded that Machine Vision Systems (MVS) have emerged as a critical component of modern industrial quality control, offering unparalleled capabilities for real-time monitoring, defect detection, and process automation. The integration of Artificial Intelligence (AI) and deep learning has further enhanced the performance and versatility of MVS, enabling them to tackle complex inspection tasks with remarkable accuracy and efficiency.