Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi, Abdul Wahab; Mali, Harlal Singh; Meena, Anoj; Hashmi, Mohammad Farukh; Bokde, Neeraj Dhanraj

doi:10.32604/cmes.2023.021223

Cited by 14 publications

(7 citation statements)

References 281 publications

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“…Machine vision measurement is a measurement method based on the principle of optical imaging, which collects images through industrial cameras, then extracts feature indices associated with surface roughness parameters from the images, and further predicts the corresponding unknown surface roughness by known image feature index values. [5] Yi et al [6] utilized the strong sensitivity of color information, using the average value of the absolute difference between the red and green components of the statistical image sampling area under an ordinary light source and macro field of view, to establish the correlation between the color difference index and surface roughness, and verified the feasibility of this method through a regression model. Ali et al [7] used the visual system, image processing tools, and the mathematical transformation method of dual orthogonal 2D wavelet transform to evaluate the roughness of the discharge machining (EDM) surface.…”

Section: Introductionmentioning

confidence: 99%

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Yi,

Feng,

Shu

2024

Advcd Theory and Sims

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The surface‐roughness machine vision measurement method requires training numerous samples with uniform roughness values to construct a prediction model. Insufficient actual sample size will make the model lack generalization ability. So, a grinding surface‐roughness measurement method combined with simulation data and transfer learning is proposed. First, through surface simulation, inverse fitting, and three‐dimensional (3D) modeling, more realistic simulation samples are obtained, real samples are prepared, and a simulation imaging system and an actual imaging experiment to acquire surface images are designed. Second, the data distribution difference between the simulation domain and the actual domain through transfer component analysis and the use of the adjusted simulation‐domain data and its label to train a support vector regression model is adjusted. Finally, the model predicts the surface roughness of the uncalibrated actual‐domain sample. For shared features of transferable simulation images and actual images, using the high sensitivity of color information, image features are enhanced and a proportion of pure color region index is designed, strongly correlated within the domain but less differentiated between domains. The results show that the transfer learning prediction accuracy based on this index exceeds 90%, verifying the feasibility of the proposed method and providing a new improvement strategy for sample expansion in visual roughness measurement.

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Section: Introductionmentioning

confidence: 99%

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Yi,

Feng,

Shu

2024

Advcd Theory and Sims

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“…It has been proven that tactile feedback enables fine grip force control of robotic devices (George et al, 2019;Chai et al, 2022) and users' attention allocation (Sklar and Sarter, 1999;Makin et al, 2023) and confers to the users the ability to recognize object size and compliance, with or without visual feedback (Arakeri et al, 2018;George et al, 2019;Chai et al, 2022). However, in terms of human-machine interaction, so far, the 2 types of typical object physical properties, surface roughness and texture, were unable to be delivered to the users through a haptic interface due to the complexity of the surface features (Bajcsy, 1973;Hashmi et al, 2023). A key reason is that the current contact sensors (e.g., pressure/ displacement transducers) cannot identify and differentiate a wide variety of features of the surface roughness and texture.…”

Section: Introductionmentioning

confidence: 99%

“…Machine vision has emerged as the most promising noncontact technique for object detection and image classification by combining cameras, videos, and deep-learning methods (Zhao et al, 2019;Georgiou et al, 2020;Hashmi et al, 2023). Recent representative studies have shown that compared to conventional methods, machine vision-based approaches could better assess the surface quality of machined parts by contactlessly measuring the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Recent representative studies have shown that compared to conventional methods, machine vision-based approaches could better assess the surface quality of machined parts by contactlessly measuring the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc. (Hashmi et al, 2023). The image calibration algorithm based on Bayes theorem has been proved can effectively restore the blurred images of object surface roughness (Dhanasekar and Ramamoorthy, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Object surface roughness/texture recognition using machine vision enables for human-machine haptic interaction

Lin,

Zheng,

et al. 2024

Front. Comput. Sci.

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Tactile feedback can effectively improve the controllability of an interactive intelligent robot, and enable users to distinguish the sizes/shapes/compliance of grasped objects. However, it is difficult to recognize object roughness/textures through tactile feedback due to the surface features cannot be acquired with equipped sensors. The purpose of this study is to investigate whether different object roughness/textures can be classified using machine vision and utilized for human-machine haptic interaction. Based on practical application, two classes of specialized datasets, the roughness dataset consisted of different spacing/shapes/height distributions of the surface bulges and the texture dataset included eight types of representative surface textures, were separately established to train the respective classification models. Four kinds of typical deep learning models (YOLOv5l, SSD300, ResNet18, ResNet34) were employed to verify the identification accuracies of surface features corresponding to different roughness/textures. The human fingers' ability to objects roughness recognition also was quantified through a psychophysical experiment with 3D-printed test objects, as a reference benchmark. The computation results showed that the average roughness recognition accuracies based on SSD300, ResNet18, ResNet34 were higher than 95%, which were superior to those of the human fingers (94% and 91% for 2 and 3 levels of object roughness, respectively). The texture recognition accuracies with all models were higher than 84%. Outcomes indicate that object roughness/textures can be effectively classified using machine vision and exploited for human-machine haptic interaction, providing the feasibility of functional sensory restoration of intelligent robots equipped with visual capture and tactile stimulation devices.

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“…With the rapid advancement of artificial intelligence technology, machine vision-based defect detection has emerged as a powerful tool for swiftly and accurately identifying product irregularities through non-contact measurements [3,4]. This technology has found widespread adoption within the domain of intelligent manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Stainless steel cylindrical pot outer surface defect detection method based on cascade neural network

Qiao,

Sun,

Cheng

et al. 2023

Meas. Sci. Technol.

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Addressing issues such as surface geometric distortion, high reflection, and the challenge of detecting minor defects on the external surfaces of stainless steel pots, this paper presents a novel approach for detecting defects on the external surfaces of cylindrical stainless steel pots based on high-resolution line-scan imaging. The method begins by creating a real-time inspection system that includes a line-scan camera and a strip light source. This system achieves distortion-free, high-resolution image acquisition of the outer surfaces of stainless steel cylindrical pots by establishing linear constraints on pot size, rotary table rotation speed, and line-scan frame rate. Subsequently, a You Only Look Once and fully convolutional network cascade neural network surface defect detection strategy is introduced, utilizing dual-channel images of the original and enhanced images as inputs. This approach enables the characterization of subtle defects in high-resolution image data. Finally, we conducted experiments using the proposed method on the provided dataset, and the results demonstrate the effectiveness of this approach in detecting various types of product defects. The mean pixel accuracy achieved a remarkable 91.69%, while the mean intersection over union score reached an impressive 83.59%. These findings provide an effective technical means for the qualitative detection of the types of defects on the surface of stainless steel pots and the quantitative measurement of the size of the defects.

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Surface Characteristics Measurement Using Computer Vision: A Review

Cited by 14 publications

References 281 publications

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

Object surface roughness/texture recognition using machine vision enables for human-machine haptic interaction

Stainless steel cylindrical pot outer surface defect detection method based on cascade neural network

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