An LED Detection and Recognition Method Based on Deep Learning in Vehicle Optical Camera Communication

Sun, Xu Andy; Shi, Wenxiao; Cheng, Qing; Liu, Wei; Wang, Zhuo; Zhang, Jiadong

doi:10.1109/access.2021.3085117

Cited by 17 publications

(5 citation statements)

References 33 publications

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“…Currently, thanks to the advances in manufacturing, deep learning has emerged as a promising candidate for detecting and tracking objects with high precision and high reliability in real-time. As demonstrated in [11], utilizing You-Only-Look-Once (YOLO) in the OCC Rx (receiver) improves the data-reception rate when compared to conventional techniques.…”

Section: Rf Link Owc Linkmentioning

confidence: 99%

Enabling Technologies and New Challenges in IEEE 802.15.7 Optical Camera Communications Standard

Nguyen,

Krishna Yoga Utama,

Jang

2024

IEEE Commun. Mag.

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In this article, we present a tutorial on recent optical camera communication (OCC) technologies that deploy light-emitting diodes (LEDs) and image sensors. The article contains a discussion on the amendment of the IEEE 802.15.7-2018 standard, termed the IEEE 802.15.7a (TG7a) Higher Rate, Longer Range OCC Task Group, which has a significant influence on the advancement of OCC technology. In addition, the tutorial article presents the enabling technical issues in TG7a and a comparison with the existing standard. The current status, new challenges, and future directions of research and development are also discussed. IntroductIonCommunication systems based on radio frequencies (RFs) have experienced substantial growth over the last decade. Qualcomm, the telecommunication company, predicts that upcoming wireless communication technologies will need to increase their capacity by a factor of a thousand [1]. However, conventional RF systems may not be able to adequately match the expected network traffic growth in the future. Hence, there is a need to explore innovative solutions that can operate within spectra that require no license (e.g., light waves). Light waves are situated at the higher end of the electromagnetic spectrum, do not contain any electromagnetic radiation, and have a bandwidth that extends considerably beyond the entire RF spectrum. Moreover, light waveforms support remarkable line of sight (LoS) separation features and can provide economical augmented-reality user experiences. Because of the increasing demands placed on wireless communication systems (such as speed, reliability, and security), the developers of wireless communication technologies are focusing on light wave communication due to the benefits of light waveforms. Accordingly, optical wireless communication (OWC) is expected to become a crucial wireless technology in the near future.In 1880, visible light communication (VLC) systems were proposed by Alexander Graham Bell in Washington, D.C., where speech was transmitted with sunlight over hundreds of meters [2]. However, the first demonstration of a VLC system was conducted in Japan ( 2004) with a high-speed communication system that used photodiodes.

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Section: Rf Link Owc Linkmentioning

confidence: 99%

Enabling Technologies and New Challenges in IEEE 802.15.7 Optical Camera Communications Standard

Nguyen,

Krishna Yoga Utama,

Jang

2024

IEEE Commun. Mag.

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“…With the continuous progress of semiconductor technology, LED gradually replaces the traditional light source and becomes an important choice for lamps [14][15][16], which provides a hardware basis for realizing VLC. When the vehicle is driving on the road, the headlamps or taillights between the front and rear vehicles can be used as the transmitter, and the receiver can be installed on another vehicle, and the light emitted by the LED can reach the receiver directly through the line-of-sight (LOS) link [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Scheme for the Number of Mirrors in Vehicular Visible Light Communication via Mirror Array-Based Intelligent Reflecting Surfaces

et al. 2022

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The optimization problem of the number of mirrors under energy efficiency (EE) maximization for vehicular visible light communication (VVLC) via mirror array-based intelligent reflecting surface (IRS) is investigated. Under considering that the formulated optimization problem is subject to the real and non-negative of the transmitted signal, the maximum power consumption satisfied luminous ability and eye safety, the minimum achievable rate, and the required bit error ratio (BER), EE is proved to be a unimodal function of the number of mirrors. Then, the binary search-conditional iteration (BSCI) algorithm is proposed for quickly finding the optimal number of mirrors with maximum EE. Numerical results demonstrate that fewer mirrors can obtain the maximum EE, and the computational complexity of the BSCI algorithm is reduced by 105 orders of magnitude, compared with the Bubble Sort method.

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“…Improving YOLOv5 model can obtain better results. Combined with a new network D2Net and YOLOv5 model [10], the test result of the blurred image has a frame rate of 36FPS. Another algorithm improved the YOLOv5 for detecting wheat spikes in UAV images, by cleaning and expanding dataset, adding micro‐scale detection layers, and adjusting the loss function.…”

Section: Introductionmentioning

confidence: 99%

Mobile phone screen surface scratch detection based on optimized YOLOv5 model (OYm)

Zhao

Zhu

Peng

2022

IET Image Processing

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To improve phone screen surface detection efficiency, an optimized YOLOv5s model (OYm) based on GhostNet(YOLOv5GHOSTs) and BottleneckCSP is proposed. For a given target sample, OYm could effectively reduce the computation of GFLOPS and detection time by optimizing the network structure. The detection results show that the mean average precision_0.5 (mAP_0.5) exceeds 95%, and the average detection rate is 16 ms. Compared with the traditional YOLOv5s model, the loss of average accuracy is ensured to be controlled within 3%, the detection frame rate of OYm is risen by 56.25%, and GFLOPS is decreased by 64.2%. The principle of OYm is explained in detail, and the proposed model is then experimentally validated.

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An LED Detection and Recognition Method Based on Deep Learning in Vehicle Optical Camera Communication

Cited by 17 publications

References 33 publications

Enabling Technologies and New Challenges in IEEE 802.15.7 Optical Camera Communications Standard

Enabling Technologies and New Challenges in IEEE 802.15.7 Optical Camera Communications Standard

An Optimal Scheme for the Number of Mirrors in Vehicular Visible Light Communication via Mirror Array-Based Intelligent Reflecting Surfaces

Mobile phone screen surface scratch detection based on optimized YOLOv5 model (OYm)

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