International Conference on Deep Learning for Image-Based Defect Detection (ICDLIBDD - 26)

This track focuses on the latest developments in deep learning architectures specifically designed for image-based defect detection. Researchers are encouraged to present novel models and modifications to existing architectures that enhance detection accuracy and efficiency.

This session will explore innovative anomaly detection techniques that leverage deep learning for identifying defects in industrial settings. Contributions should address both supervised and unsupervised learning approaches to improve defect identification.

This track emphasizes the importance of feature extraction and representation learning in the context of image-based defect detection. Papers should discuss new methodologies that enhance the interpretability and performance of defect detection systems.

This session invites research on predictive modeling techniques that integrate deep learning for quality control processes in manufacturing. Contributions should highlight how predictive analytics can lead to improved defect detection and reduced operational costs.

This track will delve into the application of convolutional neural networks (CNNs) for visual inspection tasks in various industries. Researchers are encouraged to share their findings on the effectiveness of CNNs in enhancing defect classification and detection.

This session addresses the challenges faced in developing automated defect detection systems using deep learning. Papers should propose innovative solutions and frameworks that tackle issues such as data quality, model robustness, and real-time processing.

This track explores the intersection of industrial IoT and deep learning technologies to improve defect detection processes. Contributions should focus on how IoT data can be utilized to enhance model training and defect identification.

This session will cover various data preprocessing techniques that are crucial for effective image-based defect detection. Researchers are invited to discuss methods that improve data quality and model performance through preprocessing.

This track focuses on model optimization strategies that enhance the performance of deep learning models in defect detection tasks. Papers should present novel approaches to hyperparameter tuning, architecture selection, and computational efficiency.

This session will investigate the theoretical foundations and practical applications of pattern recognition techniques in defect detection. Contributions should highlight how these techniques can be integrated with deep learning for improved outcomes.

This track emphasizes the role of visual analytics in interpreting and understanding defect detection results. Researchers are encouraged to present frameworks that combine deep learning outputs with visual analytics tools for enhanced decision-making.