Surface defect detection is crucial in industrial production, and due to the conveyor speed, real-time detection requires 30–60 frames per second (FPS), which exceeds the capability of most existing methods. This demand for high FPS has driven the need for lightweight detection models. Despite significant advancements in deep learning-based detection that have enabled single-stage models such as the you only look once (YOLO) series to achieve relatively fast detection, existing methods still face challenges in detecting multi-scale defects and tiny defects on complex surfaces while maintaining detection speed. This study proposes a lightweight single-stage detection model called lightweight industrial defect detection network with improved YOLO architecture (LIDD-YOLO) for high-precision and real-time industrial defect detection. Firstly, we propose the large separable kernel spatial pyramid pooling (SPP) module, which is a SPP structure with a separable large kernel attention mechanism, significantly improving the detection rate of multi-scale defects and enhancing the detection rate of small target defects. Secondly, we improved the Backbone and Neck structure of YOLOv8n with dual convolutional (Dual Conv) kernel convolution and enhanced the faster implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module in the Neck structure with ghost convolution and decoupled fully connected (DFC) attention, reducing the computational and parameter overhead of the model while ensuring detection accuracy. Experimental results on the NEU-DET steel defect datasets and printed circuit board (PCB) defect datasets demonstrate that compared to YOLOv8n, LIDD-YOLO improves the recognition rate of multi-scale defects and small target defects while meeting lightweight requirements. LIDD-YOLO achieves a 3.2% increase in mean average precision (mAP) on the NEU-DET steel defect dataset, reaching 79.5%, and a 2.6% increase in mAP on the small target PCB defect dataset, reaching 93.3%. Moreover, it reduces the parameter count by 20.0% and floating point operations by 15.5%, further meeting the requirements for lightweight and high-precision industrial defect detection models.

Fringe projection profilometry (FPP) is a widely used non-contact 3D measurement method. Though maturing in the last decade, it remains a significant challenge when facing the phase unwrapping of measured object surfaces in a single-shot measurement setting. With the rapid development of deep learning techniques, the adoption of a data-driven approach is gaining popularity in the field of optical metrology. This study proposes a new absolute phase recovery method based on the devised single-stage deep learning network. The aim is to ensure high-quality absolute phase recovery from a single-shot fringe projection measurement. Unlike most existing approaches, where the numerators and denominators of the wrapped phases and the fringe orders are predicted in various stages, the proposed method acquires the wrapped phases and the corresponding fringe orders within a single network, i.e. it can predict both wrapped phases and the corresponding fringe orders directly and simultaneously from the single fringe pattern projected in the single-shot mode based on a unified Y-shaped network. Experiments on benchmark datasets and models have demonstrated the effectiveness and efficiency of the technique, especially in terms of high-quality recovery of absolute phase information by using the lightweight single-stage network, and enabling the FPP-based phase 3D measurements in an online manner.

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%.