Dr. Bin Wei Profile

Dr. Bin Wei

at Xi'an Jiaotong Univ

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Publications (3)

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Proceedings Article | 28 February 2024 Paper

Research on machine-vision-based method for head-tail detection of corn ears

Hongjie Chang, Ping Wang, Huli Niu, Haipeng Yan, Bin Wei, Gaoshuai Zhang

Proceedings Volume 13071, 130713U (2024) https://doi.org/10.1117/12.3025556

KEYWORDS: Ear, Image processing, Image segmentation, Head, Cooccurrence matrices, Image filtering, Tunable filters, Image enhancement, Digital filtering, Machine vision

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To address the issues of manual feeding and placement in the processing of fresh corn ears, this study develops a fresh corn ear detection system by using machine vision and deep learning techniques. The hardware setup and acquisition of clear image data were completed. The G channel image in the RGB channel was selected for subsequent image processing. Contrast enhancement and image sharpening techniques were employed to improve the clarity of the ear region. Additionally, a median filtering method was used to eliminate noise caused by lighting conditions. Morphological processing and Otsu threshold segmentation techniques were applied to extract complete corn ear regions. Based on the contour feature, three kinds of feature extraction of ear shape, gray value, and ear texture are selected. By comparison, the ear texture feature recognition algorithm with higher recognition accuracy and shorter detection time was selected. The algorithm was used to detect the ear handle region, and the cutting position was predicted in this region. This paper studied the method of predicting the cutting position by using the slope change feature of the spikelet region contour and used C# and Halcon mixed programming to realize the automatic detection of the ear. Experimental results show that the detection accuracy of the spikelet region can reach 98.23% by using the texture feature detection algorithm, and the detection time of a single ear is 23.90 ms.

Proceedings Article | 1 September 2015 Paper

Rapid three-dimensional chromoscan system of body surface based on digital fringe projection

Bin Wei, Jin Liang, Jie Li, Maodong Ren

Proceedings Volume 9576, 95760P (2015) https://doi.org/10.1117/12.2187343

KEYWORDS: Clouds, Calibration, Projection systems, Cameras, 3D modeling, 3D acquisition, 3D image reconstruction, Digital image processing, Image processing, 3D printing

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This paper proposes a rapid body scanning system that uses optical digital fringe projection method. Twelve cameras and four digital projectors are placed around the human body from four different directions, so that the body surface threedimensional( 3D) point cloud data can be scanned in 5~8 seconds. It can overcome many difficulties in a traditional measurement method, such as laser scanning causes damage to human eye and low splicing accuracy using structured white light scanning system. First, an accurate calibration method based on close-range photogrammetry, is proposed and verified for calibrating the twelve cameras and the four digital projectors simultaneously, where a 1m×2m plate as calibration target with feature points pasted on its two-sides is used. An experiment indicates that the proposed calibration method, with a re-projection error less than 0.05pixels, has a considerable accuracy. The whole 3D body surface color point cloud data can be measured without splice different views of point cloud, because of the high accuracy calibration results. Then, in order to measure the whole body point cloud data with high accuracy, a combination of single and stereo camera measuring method, based on digital fringe projection, has presented to calculating 3D point cloud data. At last, a novel body chromoscan system is developed and a human body 3D digital model was scanned, by which a physical body model was manufactured using 3D printing technology.

Proceedings Article | 1 September 2015 Paper

Digital speckle-based stereo microscope strain measurement system for sheet metal forming by hydraulic bulge tests

Maodong Ren, Jin Liang, Lizhong Wang, Bin Wei

Proceedings Volume 9576, 95760D (2015) https://doi.org/10.1117/12.2187350

KEYWORDS: Calibration, Microscopes, Cameras, Metals, Ferroelectric LCDs, Distortion, Composites, Imaging systems, Mathematical modeling, Speckle

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A digital speckle based stereo microscope strain measurement system is developed to investigate the forming limit diagram (FLD) of miniature sheet metal under hydraulic bulge testing conditions. A stochastic speckle pattern is sprayed on the surface of the tested metal before forming. A series of images are recorded by two cameras mounted on a binocular stereo microscope during the hydroforming process. The critical major and minor strains are then calculated and plotted to construct the forming limit curve. The key technologies applied in the system are discussed in detail, including stereo microscope calibration and large deformation strain filed determination. First, considering complex optical paths and high magnification of the stereo microscope, an accurate combined distortion correction model is proposed to optimize the intrinsic and extrinsic parameters of the stereo microscope. Then, to solve the problem of strain measurement of the tested metal in large deformation situation, a large deformation measurement scheme based on deformation continuity of adjacent images is proposed. And an algorithm of limit strain determination based on spline model is proposed to calculate the critical strains at the onset of plastic instability. Finally, with our self-developed stereo microscope imaging system and sheet metal hydraulic bulging setup, FLD determination tests are conducted to validate the performance of the system. And the measured FLD is compared with the simulation results that predicted by the finite element method. The simulation and experimental results confirm that the proposed system is feasible to measure the full-field strain during the whole bulging processes and provides a better solution for forming limit diagram prediction.

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