Effectiveness of Data Augmentation for CNN-Based Pupil Center Point Detection

Kan, Naoyuki; Kondo, Nagisa; Chinsatit, Warapon; Saitoh, Tohru

doi:10.23919/sice.2018.8492593

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Future experiments will focus on studying the relationship between the amount of raw data employed by the MBOI method, as well as the number of generated synthetic images, and the associated improvement of a model after training with these data. Additionally, 3D model-based data-augmentation approaches, such as the ones presented in [10,18,20], will be explored to further improve the performance of the instrument detection algorithm.…”

Section: Discussionmentioning

confidence: 99%

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Deep-learning-based instrument detection for intra-operative robotic assistance

et al. 2022

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Purpose: Robotic scrub nurses have the potential to become an attractive solution for the operating room. Surgical instrument detection is a fundamental task for these systems, which is the focus of this work. We address the detection of the complete surgery set for wisdom teeth extraction, and propose a data augmentation technique tailored for this task. Methods: Using a robotic scrub nurse system, we create a dataset of 369 unique multi-instrument images with manual annotations. We then propose the Mask-Based Object Insertion method, capable of automatically generating a large amount of synthetic images. By using both real and artificial data, different Mask R-CNN models are trained and evaluated. Results: Our experiments reveal that models trained on the synthetic data created with our method achieve comparable performance to that of models trained on real images. Moreover, we demonstrate that the combination of real and our artificial data can lead to a superior level of generalization. Conclusion: The proposed data augmentation technique is capable of dramatically reducing the labelling work required for training a deep-learning-based detection algorithm. A dataset for the complete instrument set for wisdom teeth extraction is made available for the scientific community, as well as the raw information required for the generation of the synthetic data (https://github.com/Jorebs/Deep-learning-based-instrument-detection-for-intra operative-robotic-assistance).

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Section: Discussionmentioning

confidence: 99%

“…The generalization capacity of a model increases with the size of its training set. A common approach to artificially increase the size of a training set is data augmentation (DA) [3,10,18].…”

Section: Mask-based Object Insertionmentioning

confidence: 99%

Deep-learning-based instrument detection for intra-operative robotic assistance

et al. 2022

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“…The use of affine transformations, as depicted in Fig. 4, has been reported to outperform other data augmentation techniques [23]. The data were divided into three subsets, training (80%), validation (10%), and testing (10%) subsets.…”

Section: B Convolutional Neural Network Modelmentioning

confidence: 99%

Data-driven Analysis of Turbulent Flame Images

Roncancio¹,

Kim²,

Gamal³

et al. 2020

Preprint

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Turbulent premixed flames are important for power generation using gas turbines. Improvements in characterization and understanding of turbulent flames continue particularly for transient events like ignition and extinction. Pockets or islands of unburned material are features of turbulent flames during these events. These features are directly linked to heat release rates and hydrocarbons emissions. Unburned material pockets in turbulent CH 4 /air premixed flames with CO 2 addition were investigated using OH Planar Laser-Induced Fluorescence images. Convolutional Neural Networks (CNN) were used to classify images containing unburned pockets for three turbulent flames with 0%, 5%, and 10% CO 2 addition. The CNN model was constructed using three convolutional layers and two fully connected layers using dropout and weight decay. The CNN model achieved accuracies of 91.72%, 89.35% and 85.80% for the three flames, respectively.

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“…Finally, a note of caution: GAN-based refinement is not the only way to go. Kan et al [304] compared three approaches to data augmentation for pupil center point detection, an important subproblem in gaze estimation: affine transformations of real images, synthetic images from UnityEyes, and GAN-based refinement. In their experiments, real data augmentation with affine transformations was a clear winner, with the GAN improving over UnityEyes but falling short of the augmented real dataset.…”

Section: Case Study: Gan-based Refinement For Gaze Estimationmentioning

confidence: 99%

Synthetic Data for Deep Learning

Nikolenko¹

2019

Preprint

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Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more); we also survey the work on improving synthetic data development and alternative ways to produce it such as GANs. Second, we discuss in detail the synthetic-to-real domain adaptation problem that inevitably arises in applications of synthetic data, including syntheticto-real refinement with GAN-based models and domain adaptation at the feature/model level without explicit data transformations. Third, we turn to privacy-related applications of synthetic data and review the work on generating synthetic datasets with differential privacy guarantees. We conclude by highlighting the most promising directions for further work in synthetic data studies.

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Effectiveness of Data Augmentation for CNN-Based Pupil Center Point Detection

Cited by 8 publications

References 10 publications

Deep-learning-based instrument detection for intra-operative robotic assistance

Deep-learning-based instrument detection for intra-operative robotic assistance

Data-driven Analysis of Turbulent Flame Images

Synthetic Data for Deep Learning

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