Data augmentation by combining feature selection and color features for image classification

Meethongjan, Kittikhun; Hoang, Vinh Truong; Surinwarangkoon, Thongchai

doi:10.11591/ijece.v12i6.pp6172-6177

Cited by 2 publications

(1 citation statement)

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“…Various augmentations for image classification have been introduced [20][21][22][23]. Cutout [22] is a simple regularization method that randomly masks patches from the input image.…”

Section: Data Augmentation For Semantic Segmentationmentioning

confidence: 99%

Data Augmentation of X-ray Images for Automatic Cargo Inspection of Nuclear Items

Jang,

Lee,

et al. 2023

Sensors

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As part of establishing a management system to prevent the illegal transfer of nuclear items, automatic nuclear item detection technology is required during customs clearance. However, it is challenging to acquire X-ray images of major nuclear items (e.g., nuclear fuel and gas centrifuges) loaded in cargo with which to train a cargo inspection model. In this work, we propose a new means of data augmentation to alleviate the lack of X-ray training data. The proposed augmentation method generates synthetic X-ray images for the training of semantic segmentation models combining the X-ray images of nuclear items and X-ray cargo background images. To evaluate the effectiveness of the proposed data augmentation technique, we trained representative semantic segmentation models and performed extensive experiments to assess its quantitative and qualitative performance capabilities. Our findings show that multiple item insertions to respond to actual X-ray cargo inspection situations and the resulting occlusion expressions significantly affect the performance of the segmentation models. We believe that this augmentation research will enhance automatic cargo inspections to prevent the illegal transfer of nuclear items at airports and ports.

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“…Various augmentations for image classification have been introduced [20][21][22][23]. Cutout [22] is a simple regularization method that randomly masks patches from the input image.…”

Section: Data Augmentation For Semantic Segmentationmentioning

confidence: 99%