Review of optical detection technologies for inner-wall surface defects

A defect detection approach based on the BiFormer+MPDIoU's YOLOv8 (BM-YOLOv8) model is proposed which addresses the challenges of low accuracy and low efficiency in detecting tiny defects on the inner-wall outer surface of automotive Anti-lock Brake Systems (ABS) brake master cylinder. This method constructs an imaging model based on process parameters such as speed and inspection accuracy required during the production of automotive ABS brake master cylinder. On this basis, it employs the dynamic sparse self-attention mechanism of the BiFormer to build a network for self-attention feature extraction and fusion. It also utilizes the Minimum Point Distance Intersection over Union (MPDIoU) to optimize the bounding box regression loss function, allowing for precise detection of defects on the inner-wall outer surface of automotive ABS brake master cylinder. Both qualitative and quantitative studies demonstrated that the BM-YOLOv8 method achieves a defect identification rate of 98.8% for the inner-wall outer surface defects of automotive ABS brake master cylinder. More than 25 images per second can be detected in this process. The performance of this method meets the accuracy and real-time requirements for defect detection on the inner-wall outer surface of automotive ABS brake master cylinder.

Section: Introductionmentioning

confidence: 99%

Study on the detection technology for inner-wall outer surface defects of the automotive ABS brake master cylinder based on BM-YOLOv8

Liu,

Yan,

Meng

2024

“…In contrast, non-contact measurement methods, which are usually based on optical principles and have the advantages of superior performance, precision and efficiency, have attracted the interest of researchers in recent years [10][11][12][13]. Generally, these optical-based methods can be divided into three categories: point, line, and surface measurement, which differ from their light emitting mode [14].…”

Section: Introductionmentioning

confidence: 99%

Accuracy improvement of a multi-ring beam structured inner surface measurement: via novel calibration methodology and light source optimization

Li,

Deng,

et al. 2024

Parts with a large depth-to-diameter ratio play a critical role in the military, aerospace, and automotive industries. However, accurately measuring their inner surface profile remians challenging owing to the lack of adequate and accurate sensors. We developed a multi-ring structured light system to obtain three-dimensional data of inner contours, such as inner diameters, which are crucial for ensuring component performance and safety. In this study, we proposed three simple yet effective techniques to improve the multi-ring beam structured measurement system. First, we designed a distortion correction method to calibrate the imaging system. Second, a two-step calibration approach was used to calibrate the multi-ring projection. Meanwhile, we benchmarked the effects of different light sources on image speckles. The calibration results demonstrated that the coefficient of determination (R-2) used for line fitting exceeded 0.999. Moreover, the measurement experimental results show that the uncertainty of less than 10 µm and the smallest measurable pipe inner diameter can reach 15 mm, demonstrating that our methods are promising for improving the accuracy of structured light optical sensing systems. This system satisfies the measurement requirements and can be immediately utilized to meet the high demand for inner contour measurements in industrial applications.

“…Pipelines are widely used in terms of water supply, petroleum and natural gas transportation as key structural components [1][2][3]. Practical application requires high processing accuracy of pipeline internal surface [4].…”

Section: Introductionmentioning

confidence: 99%

Design of hexapod robot equipped with omnidirectional vision sensor for defect inspection of pipeline’s inner surface

Guo,

Liu,

Zhou

et al. 2024

Defect detection of inner surface of precision pipes is a crucial aspect of ensuring production safety. Currently, pipeline defect detection primarily relies on recording video for manual recognition, with urgent need to improve automation, quantification and accuracy. This paper presents a hexapod in-pipe robot with carrying capacity designed to transport the omnidirectional vision sensor to specified location within unreachable pipelines. The feasibility of the robot’s mechanical design and sensor load-carrying module is analyzed using theory calculations, motion simulations and finite element method. To address the challenges of small pixel ratio and weak background changes in panoramic images, a tiny defect segmentor (TDS) based on ResNet is proposed for detecting tiny defects on the inner surface of pipelines. The hardware and software systems are implemented, and the motion performance of the pipeline robot is validated through experiments. The results demonstrate that the robot achieves stable movement at a speed of over 0.1m/s and can adapt to pipe diameter ranging from of 110 to 130 mm. The novelty of the robot lies in providing stable control of the loaded vision sensor, with control precision of the rotation angle and the displacement recorded at 1.84% and 0.87%, respectively. Furthermore, the proposed method achieves a detection accuracy of 95.67% for tiny defects with a diameter less than 3 mm and provides defect location information. This pipeline robot serves as an essential reference for development of in-pipe 3D vision inspection system.