Illumination-Robust Object Coordinate Detection by Adopting Pix2Pix GAN for Training Image Generation

Huang, Yihao; Li, Chih-Hung G.; Chang, Yang-Lang

doi:10.1109/taai48200.2019.8959837

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Furthermore, a pix2pix model was proposed [29] as a general framework for image translation. Huang [30] proposed a data augmentation method utilizing a pix2pix GAN for the automatic generation of object images under various illumination effects. Nikita [31] proposed a novel strategy for data preparation based on the fusion of objects and background images to form composite data.…”

Section: Introductionmentioning

confidence: 99%

A Data Augmentation Strategy Combining a Modified pix2pix Model and the Copy-Paste Operator for Solid Waste Detection With Remote Sensing Images

Zhao

Xie

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Solid waste detection is of great significance for environmental protection. In recent years, object detection methods based on deep learning have progressed rapidly. However, it is often extremely difficult to collect sufficient data to train a model with a good performance. In this study, a data augmentation strategy was introduced to generate sufficient synthetic high-quality images for solid waste detection. First, a modified pix2pix model was proposed, in which a local-global discriminator (LGD) was designed to improve the detailed and global information of the generated images, which are commonly fuzzy with the original pix2pix model. Second, a copy-paste operator was utilized, which simply pastes the bounding box of the generated objects into different images to enhance the diversity of the samples. In this manner, the expanded dataset can be utilized to train different object detection models, for which FPN and Yolo-v4 were introduced as the validation models in this study. The experimental results show that the proposed strategy outperforms the traditional pix2pix method and the generated synthetic images can effectively improve the performance of object detection methods.

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Section: Introductionmentioning

confidence: 99%

A Data Augmentation Strategy Combining a Modified pix2pix Model and the Copy-Paste Operator for Solid Waste Detection With Remote Sensing Images

Zhao

Xie

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deep-trained illumination-robust precision positioning for real-time manipulation of embedded objects

Huang

2020

Int J Adv Manuf Technol

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An Improved Spatiotemporal Fusion Algorithm for Monitoring Daily Snow Cover Changes With High Spatial Resolution

Wang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Illumination-Robust Object Coordinate Detection by Adopting Pix2Pix GAN for Training Image Generation

Cited by 3 publications

References 6 publications

A Data Augmentation Strategy Combining a Modified pix2pix Model and the Copy-Paste Operator for Solid Waste Detection With Remote Sensing Images

A Data Augmentation Strategy Combining a Modified pix2pix Model and the Copy-Paste Operator for Solid Waste Detection With Remote Sensing Images

Deep-trained illumination-robust precision positioning for real-time manipulation of embedded objects

An Improved Spatiotemporal Fusion Algorithm for Monitoring Daily Snow Cover Changes With High Spatial Resolution

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