Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks

Yang, Jinfeng; Zhao, Zihao; Zhang, Haigang; Shi, Yihua

doi:10.1109/access.2019.2902121

Cited by 68 publications

(40 citation statements)

References 22 publications

(20 reference statements)

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“…We collect some real prohibited item images with rich shapes and poses from the internet. The foreground extracting method [24] is used to extract foreground of the natural images. Next, both the natural prohibited item images and the X-ray prohibited item images are convert to binary images.…”

Section: B the Transformation Results Based On Cycle Gan Modelmentioning

confidence: 99%

“…Each category involves 200-400 images in 256×256 size, as shown in Figure 2. In order to facilitate the X-ray image synthesis in the subsequent work, we extract the prohibited item foreground in images by the method proposed in [24]. The prohibited item images without background are shown in Figure 3.…”

Section: A X-ray Prohibited Item Image Databasementioning

confidence: 99%

“…In Ref. [24], GAN model is applied to generate X-ray prohibited item images. However, their generated images only contain the single prohibited item, while the image quality and diversity are not ideal.…”

Section: Introductionmentioning

confidence: 99%

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Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

Zhu

Zhang

et al. 2020

IEEE Access

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Recognizing prohibited items in X-ray security checking images automatically based on Convolutional Neural Networks (CNNs) has attracted attention increasingly. However, there are no suitable X-ray security checking image databases to train a reliable CNN model. Therefore, we propose a data augmentation method for X-ray security checking images. First, a lot of new X-ray prohibited item images are generated using the improved Self-Attention Generative Adversarial Network (SAGAN). Next, a Cycle GAN based method is proposed to transform the item natural images into the X-ray images. It can enrich the diversity of the new images, including item shape and pose. Then, we combine the prohibited item images with background images to synthesize the new X-ray security checking images. Finally, two single shot multi-box detector (SSD) models are applied to verify whether the enlarged database has achieved data augmentation. Experimental results show that the performance of SSD model trained by the enlarged database is better than the SSD model trained by the original database. It implies that our method can achieve data augmentation for X-ray security checking images effectively. INDEX TERMS Data augmentation, generative adversarial network, SSD, X-ray security checking image.

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Section: B the Transformation Results Based On Cycle Gan Modelmentioning

confidence: 99%

Section: A X-ray Prohibited Item Image Databasementioning

confidence: 99%

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Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

Zhu

Zhang

et al. 2020

IEEE Access

Self Cite

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“…Input images will be randomly augmented before being fed into the proposed Quantized DRCNN model. Data augmentation is a great way to increase the diversity of image datasets [36]. There are two stages in a typical CNN model, which are feature extraction and feature classification [37].…”

Section: B Deep Learning Basedmentioning

confidence: 99%

Quantized Deep Residual Convolutional Neural Network for Image-Based Dietary Assessment

2020

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Vegetable intake is an essential element to maintain a healthy body of a human. However, research shows most people do not consume an adequate intake of vegetables per day. An ameliorate dietary assessment for vegetable intake is needed to increase awareness and assist users to improve their vegetable consumption. In this paper, we proposed a novel Quantized Deep Residual Convolutional Neural Network (DRCNN) model to ameliorate the fundamental task of dietary assessment. The proposed deep learning strategy integrating a deep residual architecture and a deep learning model, i.e. convolutional neural network, to apply for image-based dietary assessment. The proposed DRCNN model is then deeply fused with posttraining quantization techniques to quantize the network weights of the proposed DRCNN model into lowbit fixed-point representations. Extensive experiments have been done to evaluate the proposed model. Results show that the proposed Quantized DRCNN model outperformed the state-of-the-arts, which include the conventional CNN models and also the machine learning models. Those experiments also indicate the effectiveness of the proposed Quantized DRCNN model. Finally, we further evaluate our model with crossdataset validation to verify the generalization of the proposed model. The experimental result proves that the proposed Quantized DRCNN model is general enough to predict unseen cases and it works well on a wide range of food images. INDEX TERMS Convolutional neural network, deep learning, deep residual network, image-based dietary assessment, machine learning, quantized model.

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“…The Generative Adversarial Network (GAN) [17] has also been used to make progress in data augmentation. GAN-based methods have produced high-quality images in some datasets [18,19]. However, the GAN-based methods are difficult to train and require a certain amount of data.…”

Section: Introductionmentioning

confidence: 99%

A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images

Yan

Tan

2019

IJGI

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In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy.

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Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks

Cited by 68 publications

References 22 publications

Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

Quantized Deep Residual Convolutional Neural Network for Image-Based Dietary Assessment

A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images

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