Hybrid CNN-LSTM Architecture for Automated Defect Detection in Industrial Surface Inspection
DOI:
https://doi.org/10.54228/mjaret0624031Keywords:
Automated Defect Detection; Industrial Surface Inspection; Hybrid CNN-LSTM; Deep Learning; Feature Extraction; Quality Control; Manufacturing Automation.Abstract
Automating defect detection in the industrial surface inspection is significant for a product quality and efficiency of the manufacturing process. Inspection techniques that used the conventional methodologies take a long time to complete and are less accurate; hence, the need to use deep learning solutions. This paper presents Hybrid CNN-LSTM Architecture which combined the CNN for spatial feature extraction and LSTM for temporal feature recognition. The employment of CNN and LSTM together improves the recognition capacity of the spatial and temporal features for better defect categories and segmentation. The experiments show that the proposed method improves the defect detection accuracy by 20% and the false positive rates by 30% than the existing method. The framework performs well in detecting the defects on various surface texture type and materials, which makes the framework to be a good solution for the industrial quality control.
Downloads
Downloads
Published
Issue
Section
License
Copyright (c) 2025 MJARET
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License Terms for MJARET
Creative Commons Attributio 4.0 International (CC BY) License:
This license allows for the following:
-
Sharing — Copy and redistribute the material in any medium or format.
-
Adaptation — Remix, transform, and build upon the material.
The license is subject to the following terms:
-
Attribution:
- You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- Attribution should include the citation of the article, the author's name, and a link to the original work published in MJARET.
- This must be done in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial:
- The material cannot be used for commercial purposes.
- Any use of the work intended to provide a commercial advantage or monetary compensation is considered outside the scope of this license.
-
No Additional Restrictions:
- You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
General Provisions:
- Understandability: This license can be revoked if you do not comply with its terms and conditions.
- Public Domain: Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- Other Rights:
- The license does not cover rights such as publicity, privacy, or moral rights that may affect your ability to use the material as contemplated by the license.
- Such rights might need to be considered and respected separately.
Disclaimer:
- MJARET does not provide any warranties with the work. The work is provided "as is" without any representations or warranties, express or implied. MJARET will not be liable for any damages resulting from the use of the work.
How to Cite:
- Proper attribution for use of the licensed work should follow the standard citation format provided by MJARET, which should include the author(s), the title of the work, MJARET, and the DOI link.
