A Real-Time Monocular Vision-Based Obstacle Detection

Wang, Szu-Hong; Li, Xiang-Xuan

doi:10.1109/iccar49639.2020.9108018

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…A relatively thick symmetric hydrofoil (NACA0016) was selected, to increase the lift-to-drag ratio and maximize the inner volume of the wing, ensuring space for the wing chambers. [17] Based on a nominal forward velocity Ṽ of the chosen reference UUV (see Section S1 and Figure S2, Supporting Information) of 0.25 m s À1 and on preliminary studies, an angle of attack (AoA) of α ¼ 12°m aximizes the efficiency of the chosen wing profile. [18] Since wings are mounted with α ¼ 0°with respect to the underwater gliders' longitudinal axis, the AoA of the vehicle coincides with that of the soft wing.…”

Section: Designmentioning

confidence: 99%

A Soft Robotic Morphing Wing for Unmanned Underwater Vehicles

Giordano,

Achenbach,

Lenggenhager

et al. 2024

Advanced Intelligent Systems

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Section: Designmentioning

confidence: 99%

A Soft Robotic Morphing Wing for Unmanned Underwater Vehicles

Giordano,

Achenbach,

Lenggenhager

et al. 2024

Advanced Intelligent Systems

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“…While lidar and millimeter-wave radar are highly accurate at detecting obstacles, their high cost limits their use in low-end vehicles [1][2][3]. Due to the low cost, high detection accuracy, and speed of vision-based obstacle identification equipment, it has become more suitable for various vehicles [4,5]. e vision-based sensor used in this study is a camera.…”

Section: Introductionmentioning

confidence: 99%

An Improved VM Obstacle Identification Method for Reflection Road

Jiang

Sang

et al. 2022

Journal of Robotics

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An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.

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“…There are three types of non-contact measurement methods: acoustic [5], optical [6] and electromagnetic methods [7], of which the optical 3D measurement is the most widely applied. Conventional optical measurement systems are Laser scanner [8], Laser radar [9], structure light scanner [10], monocular vision [11][12][13], multi-view stereo vision [14][15] and so on. Recently, neural networks have shown to superior performance in many object-detection tasks due to its ability to learn from raw data automatically [16].…”

Section: Introductionmentioning

confidence: 99%

Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

Zhang

Wang

2021

JAIT

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There are hundreds of welded studs in a car. The posture of a welded stud determines the quality of the body assembly thus affecting the safety of cars. It is crucial to detect the posture of the welded studs. Considering the lack of accurate method in detecting the position of welded studs, this paper aims to detect the weld stud’s pose based on photometric stereo and neural network. Firstly, a machine vision-based stud dataset collection system is built to achieve the stud dataset labeling automatically. Secondly, photometric stereo algorithm is applied to estimate the stud normal map which as input is fed to neural network. Finally, we improve a lightweight YOLOv4 neural network which is applied to achieve the detection of stud position thus overcoming the shortcomings of traditional testing methods. The research and experimental results show that the stud pose detection system designed achieves rapid detection and high accuracy positioning of the stud. This research provides the foundation combining the photometric stereo and deep learning for object detection in industrial production.

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A Real-Time Monocular Vision-Based Obstacle Detection

Cited by 11 publications

References 13 publications

A Soft Robotic Morphing Wing for Unmanned Underwater Vehicles

A Soft Robotic Morphing Wing for Unmanned Underwater Vehicles

An Improved VM Obstacle Identification Method for Reflection Road

Stud Pose Detection Based on Photometric Stereo and Lightweight YOLOv4

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