Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911746418016256 |
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| author | Baek, Hankyul Kim, Donghyeon Kim, Joongheon |
| author_facet | Baek, Hankyul Kim, Donghyeon Kim, Joongheon |
| contents | Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data. As convolutional neural networks (CNNs) are being optimized, the performances and computation speeds of object detection in autonomous driving have been significantly improved. However, due to the exponentially rapid growth in the complexity and scale of data used in object detection, there are limitations in terms of computation speeds while conducting object detection solely with classical computing. Motivated by this, quantum convolution-based object detection (QCOD) is proposed to adopt quantum computing to perform object detection at high speed. The QCOD utilizes our proposed fast quantum convolution that uploads input channel information and re-constructs output channels for achieving reduced computational complexity and thus improving performances. Lastly, the extensive experiments with KITTI autonomous driving object detection dataset verify that the proposed fast quantum convolution and QCOD are successfully operated in real object detection applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01370 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications Baek, Hankyul Kim, Donghyeon Kim, Joongheon Computer Vision and Pattern Recognition Emerging Technologies Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data. As convolutional neural networks (CNNs) are being optimized, the performances and computation speeds of object detection in autonomous driving have been significantly improved. However, due to the exponentially rapid growth in the complexity and scale of data used in object detection, there are limitations in terms of computation speeds while conducting object detection solely with classical computing. Motivated by this, quantum convolution-based object detection (QCOD) is proposed to adopt quantum computing to perform object detection at high speed. The QCOD utilizes our proposed fast quantum convolution that uploads input channel information and re-constructs output channels for achieving reduced computational complexity and thus improving performances. Lastly, the extensive experiments with KITTI autonomous driving object detection dataset verify that the proposed fast quantum convolution and QCOD are successfully operated in real object detection applications. |
| title | Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications |
| topic | Computer Vision and Pattern Recognition Emerging Technologies |
| url | https://arxiv.org/abs/2401.01370 |