Hana Ewada - Academia.edu (original) (raw)

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Papers by Hana Ewada

Deep learning and Domain adaptation in pedestrian detection , 2023

Pedestrian detection is a crucial task that aims to reduce the number of fatalities associated wi... more Pedestrian detection is a crucial task that aims to reduce the number of fatalities associated with pedestrian accidents. To improve the performance of pedestrian detection systems, deep learning-based models have emerged as a promising technique that can accurately and efficiently detect pedestrians in real-time. These models utilize convolutional neural networks to extract relevant features from input images and identify pedestrians. However, the problem of domain shift, where the training and testing data come from different distributions, can affect the performance of these models. To address this issue, domain adaptation techniques have been applied to pedestrian detection, enabling the model to learn from both the source and target domains. This approach can result in a more robust and accurate pedestrian detection system. The research in this field has shown that domain adaptation techniques can significantly improve the performance of pedestrian detection models, and various approaches have been proposed to enhance pedestrian detection using these methods.

Deep learning and Domain adaptation in pedestrian detection , 2023

Pedestrian detection is a crucial task that aims to reduce the number of fatalities associated wi... more Pedestrian detection is a crucial task that aims to reduce the number of fatalities associated with pedestrian accidents. To improve the performance of pedestrian detection systems, deep learning-based models have emerged as a promising technique that can accurately and efficiently detect pedestrians in real-time. These models utilize convolutional neural networks to extract relevant features from input images and identify pedestrians. However, the problem of domain shift, where the training and testing data come from different distributions, can affect the performance of these models. To address this issue, domain adaptation techniques have been applied to pedestrian detection, enabling the model to learn from both the source and target domains. This approach can result in a more robust and accurate pedestrian detection system. The research in this field has shown that domain adaptation techniques can significantly improve the performance of pedestrian detection models, and various approaches have been proposed to enhance pedestrian detection using these methods.

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