Review on Application of Machine Vision in Modern Automobile Manufacturing

Yin, Shi; Yongjie, Ren; Liu, Tao; Siyang, Guo; Jin, Zhengguo; Zhu, Jigui

doi:10.3788/aos201838.0815001

Cited by 12 publications

(3 citation statements)

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“…In outdoors, UAV can be pinpointed by global satellite navigation, vision, radar and so on. However, these methods cannot perform direct or accurate localization in indoors due to interference from factors such as building materials, light, or the environment [1][2][3]. Therefore, there is a need to propose a high-precision, wide-range, and easy-to-deploy indoor localization method [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An omnidirectional spatial monocular visual localization and tracking method for indoor unmanned aerial vehicles based on the two-axis rotary table

Wu,

Wang,

Shan

et al. 2024

Meas. Sci. Technol.

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Aiming at the complexity and poor adaptability of the calibration process in the traditional UAV indoor visual positioning, this paper proposes an omnidirectional spatial tracking and localization method for indoor UAV based on the two-axis rotary table. Firstly, the position of the UAV fuselage feature points in the camera coordinate system of the turntable camera is computed by the PNP algorithm utilizing known position information of a plurality of feature points with pixel coordinate information in the corresponding image. Pixel coordinate information is extracted by obtaining UAV body-specific feature points from rotary table camera shots. Then, UAV localization in omnidirectional space can be obtained by using the calibrated rotary axis parameters of the rotary table and the rotation angle of the rotary table and substituting them into Rodriguez's formula to unify the UAV position information acquired by the rotary table camera at different positions into a unified coordinate system. Finally, the angle at which the rotary table should rotate is calculated from the obtained UAV pose and the spatial position of the camera optical center and the rotary axis of the rotary table. The calculated angle is fed back to the turntable as feedback information. The rotary table receiving the feedback information is rotated to a position where the UAV is located at the center of the camera image. Thereby the tracking and localization of the UAV is realized. The experimental results show that the spatial range of localization is greatly expanded with the localization accuracy reaching the level of binocular visual localization. The omnidirectional spatial tracking and localization of indoor UAV can be conveniently realized by this method.

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Section: Introductionmentioning

confidence: 99%

An omnidirectional spatial monocular visual localization and tracking method for indoor unmanned aerial vehicles based on the two-axis rotary table

Wu,

Wang,

Shan

et al. 2024

Meas. Sci. Technol.

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show abstract

“…This task's complexity primarily arises from the diversity of automotive components, a wide range of sizes, and variations such as rust and corrosion under different environmental conditions. Accurate recognition of automotive components plays a crucial role in applications such as production quality control, parts inventory management, and automation assembly in the automotive industry [2]. Since 1998, when Lecun et al introduced the convolutional neural network architecture LeNet-5 and applied it for the first time to handwritten digit recognition, neural networks began to gain prominence in the field of object detection [3].…”

Section: Introductionmentioning

confidence: 99%

Research on automotive parts recognition based on improved YOLOv5 algorithm

Xu,

Hu,

Ming

et al. 2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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“…As an interdisciplinary course, Machine Vision combines AI and digital image processing. With the development of AI, machine vision can replace human beings with intelligent programs for some automated operations and measurements [5]. A complete machine vision system includes a camera and an image processing device.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course

Wang

Zhang

et al. 2023

IEEE Access

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As an interdisciplinary course, Machine Vision combines AI and digital image processing methods. This paper develops a comprehensive machine vision experiment on forest wildfire detection that organically integrates digital image processing, machine learning and deep learning technologies. Although the research on wildfire detection has made great progress, many experiments are not suitable for students to operate. Also, the detection with high accuracy is still a big challenge. In this paper, we divide the task of forest wildfire detection into two modules, which are wildfire image classification and wildfire region detection. We propose a novel wildfire image classification algorithm based on Reduce-VGGnet, and a wildfire region detection algorithm based on the optimized CNN with the combination of spatial and temporal features. The experimental results show that the proposed Reduce-VGGNet model can reach 91.20% in accuracy, and the optimized CNN model with the combination of spatial and temporal features can reach 97.35% in accuracy. Our framework is a novel way to combine research and teaching. It can achieve good detection performance and can be used as a comprehensive experiment for Machine Vision course, which can provide the support for talent cultivation in machine vision area.

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Review on Application of Machine Vision in Modern Automobile Manufacturing

Cited by 12 publications

References 0 publications

An omnidirectional spatial monocular visual localization and tracking method for indoor unmanned aerial vehicles based on the two-axis rotary table

An omnidirectional spatial monocular visual localization and tracking method for indoor unmanned aerial vehicles based on the two-axis rotary table

Research on automotive parts recognition based on improved YOLOv5 algorithm

A Deep Learning-Based Experiment on Forest Wildfire Detection in Machine Vision Course

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