With the development of intelligent manufacturing, the production and assembly accuracy of components in factories is increasing in line with growing demand. However, the traditional manual quality inspection is inefficient, inaccurate, and costly. To this end, digital and optical imaging techniques are used to achieve intelligent quality inspection. However, during the reconstruction process, the high reflectivity of object materials affects the speed and accuracy of reconstruction results. To overcome these problems, this study investigated the three-dimensional (3D) digital imaging techniques based on line laser scanning. It advances a novel methodology for image segmentation, underpinned by deep learning algorithms, to augment the precision of the reconstruction results while simultaneously enhancing processing velocity. After the reconstruction phase, the research assesses flatness tolerance using point cloud registration technology. Finally, we constructed a measurement platform with a cost of less than CNY 100,000 (about USD 14,000) and obtained a measurement accuracy of 30 microns.

Phase measurement profilometry (PMP) is primarily employed to analyze the morphology of a functional surface with precision. Historically, one of the most complex and persistent challenges in PMP has been reducing errors stemming from inconsistent indicators at the edges of a surface. In response to this challenge, we propose an optimized error compensation methodology specifically designed to handle edge artefacts. This methodology introduces the Hilbert transform and object surface albedo as tools to detect the edges of the artefact region that need to be compensated. Moreover, we analyze the characteristics of the sinusoidal fringe waveform propagation direction and investigate the reconstruction results of the fringe vertical to the current directions to compensate for edge artefacts. The experimental results for various objects show that the optimized approach can compensate for edge artefacts by projecting in two directions and reducing the projection by half. The compensated root mean square error (RMSE) for planar objects can be reduced by over 45%.

Hyperspectral imagery (HSI), with its rich spectral information across continuous wavelength bands, has become indispensable for fine-grained land cover classification in remote sensing applications. Although some existing deep neural networks have exploited the rich spectral information contained in HSIs for land cover classification by designing some adaptive learning modules, these modules were usually designed as additional submodules rather than basic structural units for building backbones, and they failed to adaptively model the spectral correlations between adjacent spectral bands and nonadjacent bands from a local and global perspective. To address these issues, a new adaptive spectral-correlation learning neural network (ASLNN) is proposed for HSI classification. Taking advantage of the group convolutional and ConvLSTM3D layers, a new adaptive spectral correlation learning block (ASBlock) is designed as a basic network unit to construct the backbone of a spatial–spectral feature extraction model for learning the spectral information, extracting the spectral-enhanced deep spatial–spectral features. Then, a 3D Gabor filter is utilized to extract heterogeneous spatial–spectral features, and a simple but effective gated asymmetric fusion block (GAFBlock) is further built to align and integrate these two heterogeneous features, thereby achieving competitive classification performance for HSIs. Experimental results from four common hyperspectral data sets validate the effectiveness of the proposed method. Specifically, when 10, 10, 10 and 25 samples from each class are selected for training, ASLNN achieves the highest overall accuracy (OA) of 81.12%, 85.88%, 80.62%, and 97.97% on the four data sets, outperforming other methods with increases of more than 1.70%, 3.21%, 3.78%, and 2.70% in OA, respectively.