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.

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° reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique.

We demonstrate a compact high-resolution spectrometer scheme using two plane gratings. In this approach, the rays are first diffracted by a fixed grating, then incident on a rotating grating at the Littrow diffraction angle, and are finally diffracted and reflected back to the fixed grating again. Thus, triple dispersion (TD) occurs during measurement, increasing the resolution. The formulae of this compact high-resolution spectrometer are rigorously derived. A design simulation with two gratings of 1050 lines/mm is performed and discussed. In addition, a prototype of this spectrometer has been built and tested. Its spectral resolution reaches a precision of 36 pm.

Echelle grating provides high spectral resolving power and diffraction efficiency in a broadband wavelength range by the Littrow mode. The spectrometer with the cross-dispersed echelle scheme has seen remarkable growth in recent decades. Rather than the conventional approach with common blazed grating, the cross-dispersed echelle scheme achieves the two-dimensional spatial distribution of the spectrum by one exposure without scanning in the broadband spectral range. It is the fastest and most sensitive spectroscopic technology as of now, and it has been extensively applied in commercial and astronomical spectrometers. In this review, we first highlight the characteristics of the echelle and then present the optical layout, detection approach, and method of calibration. Finally, we discuss the state-of-the-art implementations and applications of commercial and astronomical instruments.

We demonstrate a compact broadband high-resolution spectrometer approach. A dihedral reflector is used to reflect the dispersed light back to the grating for a second diffraction, folding the light path in a compact space, and enhancing the spectral resolution. The theoretical formulas for the system are strictly derived. In addition, a prototype of this spectrometer for fiber communication in the infrared wavelength range has been built. The optics can fit inside a volume of 12 cm × 14 cm × 5 cm and its spectral resolution is 57 pm over a wide wavelength range from 1250 nm to 1650 nm.