Design of a 360-deg panoramic capture system based on a smart phone

Song, Weitao; Liu, Xumin; Cheng, Qijia; Huang, Yetao; Zheng, Yuanjin; Liu, Yue; Wang, Yongtian

doi:10.1117/1.oe.59.1.015101

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…In terms of image acquisition, fisheye images can be extracted by precise numerical image processing algorithms [41]. Image stitching using two fisheye lenses enables a larger panoramic FoV [42]- [44]. A more compact volume can be obtained by refraction of the optical path using prisms [45], [46].…”

Section: B Fisheye Panoramic Systemmentioning

confidence: 99%

Review on Panoramic Imaging and Its Applications in Scene Understanding

Gao¹,

Yang²,

Sheng³

et al. 2022

Preprint

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With the rapid development of high-speed communication and artificial intelligence technologies, human perception of real-world scenes is no longer limited to the use of small Field of View (FoV) and low-dimensional scene detection devices. Panoramic imaging emerges as the next generation of innovative intelligent instruments for environmental perception and measurement. However, while satisfying the need for large-FoV photographic imaging, panoramic imaging instruments are expected to have high resolution, no blind area, miniaturization, and multi-dimensional intelligent perception, and can be combined with artificial intelligence methods towards the next generation of intelligent instruments, enabling deeper understanding and more holistic perception of 360 • real-world surrounding environments. Fortunately, recent advances in freeform surfaces, thinplate optics, and metasurfaces provide innovative approaches to address human perception of the environment, offering promising ideas beyond conventional optical imaging. In this review, we begin with introducing the basic principles of panoramic imaging systems, and then describe the architectures, features, and functions of various panoramic imaging systems. Afterwards, we discuss in detail the broad application prospects and great design potential of freeform surfaces, thin-plate optics, and metasurfaces in panoramic imaging. We then provide a detailed analysis on how these techniques can help enhance the performance of panoramic imaging systems. We further offer a detailed analysis of applications of panoramic imaging in scene understanding for autonomous driving and robotics, spanning panoramic semantic image segmentation, panoramic depth estimation, panoramic visual localization, and so on. Finally, we cast a perspective on future potential and research directions for panoramic imaging instruments.

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Section: B Fisheye Panoramic Systemmentioning

confidence: 99%

Review on Panoramic Imaging and Its Applications in Scene Understanding

Gao¹,

Yang²,

Sheng³

et al. 2022

Preprint

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show abstract

“…The camera module is an important component of digital products such as smartphones and personal computers [1]. In order to produce high quality and high resolution cameras, defect detection on the lens surface of camera modules is an essential process in the production process.…”

Section: Introductionmentioning

confidence: 99%

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Zhang

et al. 2022

Electronics

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For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision mAP@0.5 of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection.

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Robust locally linear embedding algorithm for machinery fault diagnosis

Zhang

Liu

2018

Neurocomputing

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Design of a 360-deg panoramic capture system based on a smart phone

Cited by 5 publications

References 20 publications

Review on Panoramic Imaging and Its Applications in Scene Understanding

Review on Panoramic Imaging and Its Applications in Scene Understanding

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Robust locally linear embedding algorithm for machinery fault diagnosis

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