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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.19379 |
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| _version_ | 1866913871815507968 |
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| author | Chen, Ji Wu, Yue Li, Muyang Yuan, Zhongyi Zhou, Zi-Wen Hao, Cheng-Yao Zhu, Bingcheng Wang, Yin Ji, Jitao Huang, Chunyu Li, Haobai Zhang, Yanxiang Qiu, Kai Zhu, Shining Li, Tao Zhang, Zaichen |
| author_facet | Chen, Ji Wu, Yue Li, Muyang Yuan, Zhongyi Zhou, Zi-Wen Hao, Cheng-Yao Zhu, Bingcheng Wang, Yin Ji, Jitao Huang, Chunyu Li, Haobai Zhang, Yanxiang Qiu, Kai Zhu, Shining Li, Tao Zhang, Zaichen |
| contents | Intelligent object detection, which extracts crucial information like targets categories and locations, plays a vital role in emerging technologies including autonomous driving, the Internet of Things, and next-generation mobile communication systems. With the advancement of intelligent object detectors towards higher integration and miniaturization, their portability and adaptability to a broader range of scenarios have been significantly enhanced. However, this progress comes at the cost of reduced detection quality and narrower field-of-view, which severely impacts overall performances. Here we present a neural nanophotonic object detector based on a metalens array, capable of delivering high-quality imaging with an ultra-wide field-of-view of 135°. The combined neural network not only further improves the imaging quality, but also enables the detector to achieve high-precision target recognition and localization. Moreover, we integrated the neural nanophotonic object detector into a miniature unmanned aerial vehicle to enable wide-angle imaging and intelligent recognition of various real-world dynamic objects, demonstrating the high mobility and flexibility of our neural nanophotonic object detector. Our study presents a systematic framework for advancing revolutionary intelligent detection systems, offering significant potential for a wide range of future applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural nanophotonic object detector with ultra-wide field-of-view Chen, Ji Wu, Yue Li, Muyang Yuan, Zhongyi Zhou, Zi-Wen Hao, Cheng-Yao Zhu, Bingcheng Wang, Yin Ji, Jitao Huang, Chunyu Li, Haobai Zhang, Yanxiang Qiu, Kai Zhu, Shining Li, Tao Zhang, Zaichen Optics Intelligent object detection, which extracts crucial information like targets categories and locations, plays a vital role in emerging technologies including autonomous driving, the Internet of Things, and next-generation mobile communication systems. With the advancement of intelligent object detectors towards higher integration and miniaturization, their portability and adaptability to a broader range of scenarios have been significantly enhanced. However, this progress comes at the cost of reduced detection quality and narrower field-of-view, which severely impacts overall performances. Here we present a neural nanophotonic object detector based on a metalens array, capable of delivering high-quality imaging with an ultra-wide field-of-view of 135°. The combined neural network not only further improves the imaging quality, but also enables the detector to achieve high-precision target recognition and localization. Moreover, we integrated the neural nanophotonic object detector into a miniature unmanned aerial vehicle to enable wide-angle imaging and intelligent recognition of various real-world dynamic objects, demonstrating the high mobility and flexibility of our neural nanophotonic object detector. Our study presents a systematic framework for advancing revolutionary intelligent detection systems, offering significant potential for a wide range of future applications. |
| title | Neural nanophotonic object detector with ultra-wide field-of-view |
| topic | Optics |
| url | https://arxiv.org/abs/2505.19379 |