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

5th - 6th December 2026 Istanbul, Turkey
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Join researchers, academicians, professionals, and industry experts to exchange ideas, strengthen academic collaboration, and explore research-driven practices across diverse disciplines.

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4 Terms & Condition
Refund, Cancellation, Postponement, Travel and Transfer of Registration:
Refund Policy:

If the registrant is unable to attend and is not in a position to transfer his/her participation to another person or event, then the following refund arrangements apply: Keeping given advance payments towards Venue, Printing, Shipping, Hotels, and other overheads, we had to keep the Refund Policy as the following slabs:

1. Cancellations made before 60 days of the conference are eligible for a full refund, subject to a $100 cancellation charge.
2. Cancellations made within 60-30 days of the conference are eligible for a 50% payment refund.
3. Cancellations made within 30 days of the conference are not eligible for a refund.
4. Virtual Participation registrations are non-refundable.

(a) The above refund policies are applicable only if the registrant has not received the official invitation letter for participation in the event, If the client receives the conference invitation letter but cannot attend the conference, no refund will be provided.

(b) If the client cannot attend the conference for any personal reasons, the registration fee is not refundable. However, the fee will be considered as a credit to participate in any of our international conferences within one year from the date of registration.

(c) In the event of a visa denial, travel reasons, or natural calamities preventing attendance, the registration fee will not be refunded. However, the fee will be considered as a credit to participate in any of our international conferences within one year from the date of registration.

(d) Since this conference is hybrid, the organizer reserves the right to conduct it in any format, physical or virtual. Refunds will not be provided for format changes.

(e) For participants who register close to the conference date or after the standard registration deadline, The registration will be considered for virtual participation only, as the conference is in hybrid mode. However, you will be eligible to attend one more international conference based on your registration category.

Cancellation Policy:

If the organizer cancels this event for any reason, you will receive a credit for 100% of the registration fee paid. You may use this credit for another event which must occur within one year from the date of cancellation.

Postponement of Event:

If the organizer postpones an event for any reason and you are unable or unwilling to attend on rescheduled dates, you will receive a credit for 100% of the registration fee paid. You may use this credit for another event which must occur within one year from the date of postponement.

Travel and Accommodation Responsibilities:

a) The responsibility for making and managing travel arrangements, including flights, transportation, and accommodation, lies solely with the individual participant.

b) In the event of any changes to the conference format, cancellation, or venue adjustments, the organizers cannot assume responsibility for any costs incurred by participants for travel or accommodation.

Transfer of registration:

All fully paid registrations are transferable to other persons from the same organization if the registered person is unable to attend the event. Transfers must be made by the registered person in writing to event@after.org.in. Details must include the full name of the replacement person, their title, contact phone number, and email address. All other registration details will be assigned to the new person unless otherwise specified. Registration can be transferred from one conference to another conference of the same organizer if the registrant is unable to attend one of the conferences

However, Registration cannot be transferred if it is intimated within 14 days of the respective conference. The transferred registrations will not be eligible for Refund.

Visa Information:

The organizer will not directly contact embassies and consulates on behalf of visa applicants. All delegates or invitees should take responsibility for their visa and travel arrangements. Important note for failed visa applications: Visa issues cannot come under the consideration of the cancellation policy of the organizer, including the inability to obtain a visa.

N.B:

All cancellations or modifications of registration must be made in writing to event@after.org.in

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Conference Session Tracks

Aligned with United Nations Sustainable Development Goals

Our conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.

SDG 4 SDG 8 SDG 9 SDG 11 SDG 12
01 Advancements in Deep Learning Architectures for Defect Detection
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.
02 Anomaly Detection Techniques in Industrial Applications
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.
03 Feature Extraction and Representation Learning in Computer Vision
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.
04 Predictive Modeling for Quality Control in Manufacturing
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.
05 Convolutional Neural Networks for Visual Inspection
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.
06 Automated Defect Detection Systems: Challenges and Solutions
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.
07 Industrial IoT and Deep Learning for Enhanced Inspection
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.
08 Data Preprocessing Techniques for Image-Based Analysis
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.
09 Model Optimization Strategies for Defect Detection
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.
10 Pattern Recognition in Defect Detection: Theory and Applications
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.
11 Visual Analytics for Defect Detection Insights
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.
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