TY - GEN
T1 - Surface Defect Detection Using Deep Learning
T2 - 7th International Conference on Artificial Intelligence and Virtual Reality, AIVR 2023
AU - Dharma, Fajar Pitarsi
AU - Singgih, Moses Laksono
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Deep learning
KW - Emerging trends
KW - Image processing
KW - Manufacturing industries
KW - Research gaps
KW - Surface defect detection
UR - http://www.scopus.com/inward/record.url?scp=85190657153&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-9018-4_18
DO - 10.1007/978-981-99-9018-4_18
M3 - Conference contribution
AN - SCOPUS:85190657153
SN - 9789819990177
T3 - Smart Innovation, Systems and Technologies
SP - 247
EP - 260
BT - AI Technologies and Virtual Reality - Proceedings of 7th International Conference on Artificial Intelligence and Virtual Reality AIVR 2023
A2 - Nakamatsu, Kazumi
A2 - Patnaik, Srikanta
A2 - Kountchev, Roumen
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 July 2023 through 23 July 2023
ER -