RGBD-SLAM Based on Object Detection With Two-Stream YOLOv4-MobileNetv3 in Autonomous Driving

Li, Gongfa; Fan, Hanwen; Jiang, Guozhang; Jiang, Du; Liu, Yuting; Tao, Bo; Yun, Juntong

doi:10.1109/tits.2023.3284228

Cited by 8 publications

(2 citation statements)

References 57 publications

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“…In this work, the fitting ability of the PADM was evaluated by the PV and RMS of the residual surface shape ΔF(x, y). [77][78][79][80] ΔΦ(x, y) = Φ(x, y) − Ψ(x, y), (7) F I G U R E 9 FEA simulation for hexagonal (A) and square configuration (B).…”

Section: Algorithm For Voltage Calculationmentioning

confidence: 99%

Design of novel deformable mirror with large displacement

Li,

Tian,

Wang

et al. 2023

Concurrency and Computation

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SummaryA stabilized zoom system with deformable mirrors (DMs) was designed for continuous optical zoom. In order to adjust the focal length and expand the field angle, the characteristic surface shapes of the DMs composed of the low‐order and high‐order Zernike polynomials were provided. The maximum peak‐to‐valley (PV) value at the center of the surface shape is 80 μm. This work presents a piezoelectrically‐actuated deformable mirror (PADM) with large displacement for a stabilized zoom system. The COMSOL simulation was conducted to obtain the desired design by matching and optimizing structural parameters. The maximum displacement of PADM was more than 80 μm. The fitting results by the steepest descent algorithm (SD) showed that the RMS values of the residual surface shapes were 1 of 7 to 1 of 4 of their PV values, and the PV values of the residual surface shapes were less than 1 of 10 of the PV values of the target surface shapes.

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Section: Algorithm For Voltage Calculationmentioning

confidence: 99%

Design of novel deformable mirror with large displacement

Li,

Tian,

Wang

et al. 2023

Concurrency and Computation

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show abstract

“…Some studies, such as geographical distance, 24 metro points of interest, 25 and passenger flow profile similarity, 26 have provided external insights to better capture the potential spatial dependencies hidden in passenger flow data 27,28 . However, these predefined static maps tend to ignore the time‐varying nature of the spatial correlation of site flows and fail to capture the potential spatial dependencies implicit in the dynamic flow data 29,30 . In addition, the construction of passenger flow prediction models should fully consider the complex spatial correlation and conduct in‐depth studies on temporal correlation 31 .…”

Section: Introductionmentioning

confidence: 99%

A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

Xi,

Yan,

Jia

et al. 2024

Concurrency and Computation

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SummaryPassenger flow prediction is an important part of daily metro operation, and its accuracy affects the deployment of train resources management. Due to the complex spatiotemporal correlation characteristics of metro passenger flow, it is necessary to describe it to improve the accuracy of passenger flow prediction. However, the existing models mainly construct the weight matrix based on the static graph and the similarity between stations when describing the spatial correlation of station passenger flow but ignore the time‐varying characteristics of the spatial correlation of station passenger flow. To address this problem, this study introduces a dynamic multi‐graph and multidimensional attention spatiotemporal model. Specifically, the Graph Convolutional Neural Network combined with dynamic multigraph extracts spatial features and the Gated Recurrent Unit extracts temporal features of passenger flow. The multidimensional attention can obtain the spatiotemporal correlation of passenger flow data by assigning weights to them. Finally, this model has been used to conduct experiments on Beijing metro passenger flow datasets with time granularity of 10 and 15 min. The result indicates that the DGMANN model outperforms state‐of‐the‐art other deep learning methods in passenger flow prediction. In addition, the effectiveness of its key submodules has been verified through ablation experiments.

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Research on Road Object Detection Model Based on YOLOv4 of Autonomous Vehicle

Wang,

Liu

et al. 2024

IEEE Access

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The YOLOv4 network is widely used in object detection tasks as a representative network, but there is also the problem that the complexity of the network model affects the detection speed. In this paper, we propose an improved MV2_S_YE object detection algorithm based on the YOLOv4 network to improve the detection accuracy while increasing the road object detection speed. Firstly, the backbone network CSPDarknet53 of the YOLOv4 network is replaced by the Mobilenetv2 network to reduce the number of parameters of the network; secondly, the channel attention mechanism is introduced, and the SENet module is embedded in the structure of the PANet to optimize the object detection accuracy; finally, the EIOU loss function is used to replace the CIOU loss function to improve the object detection accuracy further. The MV2_S_YE network is obtained and tested on Pascal VOC, Udacity, and KAIST datasets. To evaluate our approach, we compared MV2-S-YE with YOLOv4, YOLOv4-tiny, YOLOv7-tiny and YOLOv8s. The results show that MV2-S-YE 的

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RGBD-SLAM Based on Object Detection With Two-Stream YOLOv4-MobileNetv3 in Autonomous Driving

Cited by 8 publications

References 57 publications

Design of novel deformable mirror with large displacement

Design of novel deformable mirror with large displacement

A dynamic multigraph and multidimensional attention neural network model for metro passenger flow prediction

Research on Road Object Detection Model Based on YOLOv4 of Autonomous Vehicle

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