Surface Defect Detection Using Deep Learning: A Comprehensive Investigation and Emerging Trends

Surface defect detection is currently a topic that contributes important things in identifying and assessing defects based on surface appearances, finding widespread applications in diverse manufacturing industries. This approach involves the effective handling and analysis of surface appearances using image processing techniques, coupled with the utilization of deep learning methods for defect detection in several materials such as fabric, steel, aluminum, welding, and others. However, the existing research in this field is confronted with several limitations pertaining to the accuracy, speed, and balance of defect detection outcomes. In response to these challenges, this research paper presents a comprehensive investigation into deep learning techniques for surface defect detection in some applications in industries. With the growing demand for efficient and accurate defect detection in various industries, this study aims to explore the current state of research, identify key research gaps, and shed light on the emerging trends in leveraging deep learning for surface defect detection. Through a meticulous review investigation of relevant literature and an in-depth analysis of existing studies, this research provides valuable insights into the advancements, challenges, and potential future directions in this topic area.