Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP)

Lin, Dong-Chang; Chow, Chi‐Wai; Peng, Ching-Wei; Hung, Tun-Yao; Chang, Yun-Han; Song, Shao-Hua; Lin, Yun-Shen; Liu, Yang; Lin, Kon‐Ping

doi:10.1109/jlt.2021.3103707

Cited by 23 publications

(4 citation statements)

References 33 publications

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“…In [85] a combination of a ResNet and transfer learning (TL) is implemented, and compared to LR, to estimate the 2D position of a test-bed PD receiver. To obtain the RSS values, that are utilized as inputs in the approach,a scheme for duplicating a positioning unit cell model is developed and demonstrated experimentally.…”

Section: ) DL Methodsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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Section: ) DL Methodsmentioning

confidence: 99%

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Rekkas,

Iliadis,

Sotiroudis

et al. 2023

IEEE Open J. Commun. Soc.

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“…Object detection algorithms based on neural networks have been applied in various fields [14][15][16][17][18][19][20][21]. For instance, in [22], the authors proposed a deep learning-based object detection approach that detects objects from the acquired disk image of the suspect machine to make the forensic investigation process fast, efficient, and robust.…”

Section: Related Workmentioning

confidence: 99%

Multi‐object road waste detection and classification based on binocular vision

Guo,

Chen

2024

The Journal of Engineering

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A road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot.

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“…For the genetic algorithm, particle swarm optimization algorithm, cuckoo algorithm, whale algorithm and other metaheuristic algorithms [20][21][22][23], significant computational power of the receiving terminal is required, and a random number of iterations are needed for each positioning. Regarding another machine learning algorithm based on neural networks [24][25][26], it suffers from poor portability. If the indoor environment is changed, the network model needs to be re-generated, and if the positioning accuracy is to be improved, the network complexity will increase exponentially.…”

Section: Motivationmentioning

confidence: 99%

Indoor Visible Light Positioning System Based on Point Classification Using Artificial Intelligence Algorithms

Long

Zhang

Cao

et al. 2023

Sensors

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In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy.

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Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP)

Cited by 23 publications

References 33 publications

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review

Multi‐object road waste detection and classification based on binocular vision

Indoor Visible Light Positioning System Based on Point Classification Using Artificial Intelligence Algorithms

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