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Vol.13, No.2, May 2024. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
An Optimized Mask R-CNN with Bag-of-Visual Words and Fast+Surf Algorithm in Sharp Object Instance Segmentation for X-ray Security
Edgardo Jr S. Abong, Karelle Teyle A. Janducayan, Jomer Mae M. Lima, Meljohn V. Aborde
© 2024 Meljohn V. Aborde, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 13, Issue 2, Pages 926-939, ISSN 2217-8309, DOI: 10.18421/TEM132-09, May 2024.
Received: 01 October 2023. Revised: 10 January 2024.
Abstract:
Automated security X-ray analysis is highly desired for efficiently inspecting sharp objects. The research formulated an optimized approach for sharp object detection using a Mask R-CNN architecture. The dataset used during the training phase consists of 238 balanced raw images extracted from GitHub named OPIXray. The researchers utilized recent advances in computer vision algorithms, including the Bag-of-Words and Fast+Surf feature extraction techniques, to improve the accuracy and reliability of object deletion. The research demonstrated that the optimized versions of the classification and object detection models have significantly improved accuracy for most categories, with a 5% improvement for the clear category and a 3% improvement for both the scissor and straight knife detection.
Keywords –Mask R-CNN, Bag-of-Visual-Words, Fast-Surf, x-ray scanning, detection. |
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