Search

Metal component surfaces are random textured and non-smooth. There are many stains on the surface of metal component that are similar to the gray scale of the scratches. The scratches have non-uniform gray distribution, various shapes, and low contrast in their background, posing challenges in accurate scratch detection. This paper presents a method for detecting weak scratches on metal component surfaces based on deep convolutional neural networks (DCNNs). First, a DCNN is trained using labeled scratch images. Then, the scratches and some faults are detected by the trained DCNN, and most of the faults can be removed through properly thresholding based on the size of connected regions. Finally, the scratch length united in the number of pixels is obtained by the skeleton extraction. The experimental results show that the proposed method can effectively deal with background noise, thereby achieving accurate scratch detection.

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This paper builds a convenient and economic multi-view stereo (MVS) measurement system based on a linear stage and a rotary stage to reconstruct the measured object surface completely and accurately. In the proposed technique, the linear stage is used to generate the trigger signal and synchronize the laser sensor scanning, the rotary stage is used to rotate the object and obtain multi-view point cloud data, and then the multi-view point cloud data are registered and integrated into a 3D model. The measurement results show a measurement accuracy of 0.075 mm for a 360&deg, reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique.

This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%, the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

The non-contact three-dimensional measurement and reconstruction techniques have played a significant role in the packaging and transportation of precious cultural relics. This paper develops a structured light based three-dimensional measurement system, with a low-cost for cultural relics packaging. The structured light based system performs rapid measurements and generates 3D point cloud data, which is then denoised, registered and merged to achieve accurate 3D reconstruction for cultural relics. The multi-frequency heterodyne method and the method in this paper are compared. It is shown that the relative accuracy of the proposed low-cost system can reach a level of 1/1000. The high efficiency of the system is demonstrated through experimental results.

Infrared spectrum analysis technology can perform fast and nondestructive detection of gas and has been widely used in many fields. This work studies the quantitative analysis technology of the infrared spectrum based on deep learning. The experimental results show that the quantitative analysis model of logging gas established here can reach 100% recognition accuracy for elemental gas; further, the accuracy rate of spectral of mixed gas recognition reached 98%, indicating that the infrared spectrum logging gas detection model based on deep learning can quickly and accurately perform quantitative analysis of logging gas.

To deal with the three-dimensional (3-D) point cloud loss caused by object reflection in the active fringe projection 3-D measurement, an active reflection suppression method for 3-D measurement is proposed. The method employs high-dynamic range images obtained by multiple exposure image fusion and a three-wavelength phase-shift profilometry method to achieve high-precision 3-D measurement of reflective objects. Experimental results show that, compared to traditional 3-D measurement methods, the proposed one can more effectively handle reflections thereby avoiding 3-D point cloud loss in the measurement of reflective objects.

CdS nano-particulate films were prepared at the air/water interface under Langmuir monolayers of arachidic acid (AA) via interfacial reaction between Cd 2+ ions in the subphase and H 2 S molecules in the gaseous phase. The films were made up of fine CdS nanoparticles with hexagonal Wurtzite crystal structure after reaction. It was revealed that the formation of CdS nano-particulate films depends largely on the experimental conditions. When the films were ripened at room temperature or an increased temperature (60 °C) for one day, numerous holes were appeared due to the dissolution of smaller nanoparticles and the growth of bigger nanoparticles with an improved crystallinity. When the films were ripened further, CdS rodlike nanoparticles with cubic zinc blende crystal structure appeared due to the re-nucleation and growth of CdS nanoparticles at the stacking faults and defect structures of the hexagonal CdS grains. These structures were characterized by transmission electron microscopy (TEM), high-resolution TEM (HRTEM), and X-ray diffraction (XRD). These results declare that CdS semiconductor nanoparticles formed at the air/water interface change their morphologies and crystal structures during the ripening process due to dissolution and recrystallization of the particles.