Speed Bump Segmentation an Application of Conditional Generative Adversarial Network for Self-driving Vehicles

Patil, Sandip Omprakash; Variyar, V. V. Sajith; Soman, K P

doi:10.1109/iccmc48092.2020.iccmc-000173

Cited by 22 publications

(3 citation statements)

References 10 publications

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“…The network structure incorporates skip-layer excitation in the generator and self-supervised autoencoder layer in the discriminator. These adjustments significantly enhance the speed of high-resolution training with modest computational requirements [17], [18]. The reader is referred to Patil et al [17] for information on the LGAN generator and discriminator architecture.…”

Section: ) Gan Structure and Parameter Selectionmentioning

confidence: 99%

“…These adjustments significantly enhance the speed of high-resolution training with modest computational requirements [17], [18]. The reader is referred to Patil et al [17] for information on the LGAN generator and discriminator architecture. The LGAN training parameters are listed in Table 2.…”

Section: ) Gan Structure and Parameter Selectionmentioning

confidence: 99%

“…On the other hand, the discriminator learns to differentiate between real images and those made by the generator. The antagonistic interplay between the generator and discriminator gradually improves both networks until the generator produces impersonations of such high quality that the discriminator accepts them as originals [17], [18].…”

Section: Introductionmentioning

confidence: 99%

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Lightweight Generative Adversarial Network Fundus Image Synthesis

Aziz

Sulaiman

Zabidi

et al. 2022

JOIV : Int. J. Inform. Visualization

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Blindness is a global health problem that affects billions of lives. Recent advancements in Artificial Intelligence (AI), (Deep Learning (DL)) has the intervention potential to address the blindness issue, particularly as an accurate and non-invasive technique for early detection and treatment of Diabetic Retinopathy (DR). DL-based techniques rely on extensive examples to be robust and accurate in capturing the features responsible for representing the data. However, the number of samples required is tremendous for the DL classifier to learn properly. This presents an issue in collecting and categorizing many samples. Therefore, in this paper, we present a lightweight Generative Neural Network (GAN) to synthesize fundus samples to train AI-based systems. The GAN was trained using samples collected from publicly available datasets. The GAN follows the structure of the recent Lightweight GAN (LGAN) architecture. The implementation and results of the LGAN training and image generation are described. Results indicate that the trained network was able to generate realistic high-resolution samples of normal and diseased fundus images accurately as the generated results managed to realistically represent key structures and their placements inside the generated samples, such as the optic disc, blood vessels, exudates, and others. Successful and unsuccessful generation samples were sorted manually, yielding 56.66% realistic results relative to the total generated samples. Rejected generated samples appear to be due to inconsistencies in shape, key structures, placements, and color.

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Section: ) Gan Structure and Parameter Selectionmentioning

confidence: 99%