Detection of Bubble Defects on Tire Surface Based on Line Laser and Machine Vision

Yang, Hualin; Jiang, Yuanzheng; Deng, Fang; Mu, Yusong; Zhong, Yan; Jiao, Dongmei

doi:10.3390/pr10020255

Cited by 7 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The longitudinal rip of conveyor belt detection was performed by Xianguo Li et al using laser-based machine vision in [12] because of the use of such belt mechanisms to transfer materials or products. Other than that, bubble defects on tire surface were also detected by Hualin Yang et al in [13] by using laser and machine vision. Daniel Lopez-Escogido et al [14], detected small defects in PCB using 2-D high precision laser sensors, which investigated high errors of units of micrometers due to the small outline of Surface-Mount-Devices (SMD).…”

Section: Introductionmentioning

confidence: 85%

Internal Works Quality Assessment for Wall Evenness using Vision-based Sensor on a Mecanum-Wheeled Mobile Robot

Shukor¹,

Jamaluddin²,

Ramli³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Robotics in the construction industry has been used for a few decades up to this present time. There are various advanced robotics mechanisms or technologies developed for specific construction task to assist construction. However, not many researches have been found on the quality assessment of the finished structures. This research proposes a quality assessment robot that will assist in performing the assessment of the internal works of a building by assessing a quality assessment criterion in the Malaysian Construction Industry Standards. There are various assessment criteria such as hollowness, cracks and damages, finishing and jointing. This paper will focus on the wall evenness using a camera mounted on a mobile robot with a Mecanum wheel design. The wall evenness assessment was done via projecting a laser leveler on the wall and capturing the images by using a camera, which is later processed by a central controller. Results show that the deviation calculation method can be used to differentiate between even and uneven walls. Pixel deviations for even walls show values of less than 15 while uneven walls show values of more than 20 pixels.

show abstract

Section: Introductionmentioning

confidence: 85%

Internal Works Quality Assessment for Wall Evenness using Vision-based Sensor on a Mecanum-Wheeled Mobile Robot

Shukor¹,

Jamaluddin²,

Ramli³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…When the image features are vague, the complexity of image preprocessing will increase accordingly, and light source compensation is required to enhance the target information during image acquisition. In this paper, a linear laser is used for compensation, and the sipe feature is enlarged by irradiating the sipe area [12]. In addition, the tires are shaded to reduce ambient light interference.…”

Section: The Principle Of Mvrmentioning

confidence: 99%

Research on Tire Groove Depth Detection System based on MVR

Qian¹,

Xingwen²,

Zhu³

et al. 2022

Preprint

View full text Add to dashboard Cite

The change of tire groove depth will have a huge impact on tire performance, and the use of excessively worn tires is not conducive to the driving safety of automobiles. Tire groove depth detection has become one of the annual inspection items of automobiles, but the research on its related detection technology is still relatively backward. Based on the principle of monocular vision ranging (MVR), image processing technology and cloud platform technology, this paper develops a tire groove depth detection system, which realizes non-destructive detection of tire groove depth. In addition, the system uses the cloud platform to store the test results, and builds a multi-level data management system, allowing car owners to keep track of the tire wear status and historical changes, which is of great significance to ensuring driving safety.

show abstract

“…Line-structured light is widely used in 3D measurement fields, such as computer vision, bridge tracking, construction monitoring, and unmanned vehicles. [1][2][3][4] Generally, the structure of a light stripe can be divided into arcs, acute angles, and straight lines. 5 Existing line-structured light center extraction algorithms can be roughly divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

Tang,

Jiang,

Miao

et al. 2023

Opt. Eng.

View full text Add to dashboard Cite

Common line-structured light center extraction algorithms have limitations in accuracy and robustness when extracting the center of line-structured light obtained from objects with complex surface structures. In this study, we propose a high-precision center extraction algorithm for multi-curvature line-structured light. This algorithm improves the enhanced parallel thinning algorithm to eliminate redundant points and burrs and fills for missing center points at the end of the light stripe via the improved internal propulsion center extraction algorithm. Finally, sub-pixel centers are recalculated in the direction of the normal vector. A comparative experiment using three classical algorithms was conducted based on a tooth model with a complex curved surface. The experimental results show that the mean absolute error of this algorithm is <0.1, which is only half that of the best-performing dual-threshold grayscale center-of-gravity method in the classical method. Extraction of the center can be performed for multi-curvature line-structured light over 20 curvatures in the range of ð0.0001; 4.1439Þ∕pixel −1 ; moreover, the end of the light stripe and the identification of the truncation can be improved. Therefore, the proposed algorithm is suitable for contour detection of multi-curvature targets and effectively improves the accuracy of high-precision object detection.

show abstract

Detection of Bubble Defects on Tire Surface Based on Line Laser and Machine Vision

Cited by 7 publications

References 33 publications

Internal Works Quality Assessment for Wall Evenness using Vision-based Sensor on a Mecanum-Wheeled Mobile Robot

Internal Works Quality Assessment for Wall Evenness using Vision-based Sensor on a Mecanum-Wheeled Mobile Robot

Research on Tire Groove Depth Detection System based on MVR

High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

Contact Info

Product

Resources

About