Rapid and accurate classification of surface defects in the strip steel production process contributes to reducing production costs in factories. In this article, a dual feature-integration network (DFINet) is constructed for faster and more pragmatic few-shot strip steel surface defect classification scenarios. The model utilizes a relatively simple convolutional neural network (CNN) as the backbone to improve real-time performance. Bidirectional long short-term memory networks are employed to extract cross-image features (CIFs) from the support set, which are, then, fused with the structural features extracted by the backbone to obtain aggregated features for updating deeper support set features. The positions of support samples are adjusted by randomly cropping them into several subimages and fusing the features of these subimages to further update the positions of support samples in the metric space. Leveraging the nonparametric structure and strong adaptability of Euclidean distance, the model employs it as a classifier to further enhance real-time and classification performance. Additionally, to simulate more pragmatic few-shot strip steel surface defect classification scenarios, a modification is made to the common N-way, K-shot training mode, by changing the number of samples in the support set from fixed-shot to Random-shot. Extensive experiments demonstrate that the proposed method can rapidly and accurately classify surface defects in strip steel, even in more challenging few-shot classification scenarios.

Strip steel plays a crucial role in modern industrial production, where enhancing the accuracy and real-time capabilities of surface defect classification is essential. However, acquiring and annotating defect samples for training deep learning models are challenging, further complicated by the presence of redundant information in these samples. These issues hinder the classification of strip steel surface defects. To address these challenges, this paper introduces a high real-time network, ODNet (Orthogonal Decomposition Network), designed for few-shot strip steel surface defect classification. ODNet utilizes ResNet as its backbone and incorporates orthogonal decomposition technology to reduce the feature redundancies. Furthermore, it integrates skip connection to preserve essential correlation information in the samples, preventing excessive elimination. The model optimizes the parameter efficiency by employing Euclidean distance as the classifier. The orthogonal decomposition not only helps reduce redundant image information but also ensures compatibility with the Euclidean distance requirement for orthogonal input. Extensive experiments conducted on the FSC-20 benchmark demonstrate that ODNet achieves superior real-time performance, accuracy, and generalization compared to alternative methods, effectively addressing the challenges of few-shot strip steel surface defect classification.

Distributed laser measurement systems, widely used in high-end equipment such as airplanes, ships, and other manufacturing fields, face challenges in large spatial measurements due to laser plane obstructions and weak intersections. This paper introduces a novel omnidirectional sensor with enhanced adaptability to complex environments and improved measurement accuracy. Initially, an integrated omnidirectional measurement model is established, followed by the analysis of the optical path of the front-end detector, and the design of a signal-conditioning circuit for the photoelectric conversion of the front-end laser signal, Subsequently, a circuit testing platform is established to validate the detection functionality, and the corresponding results indicate that the symmetry of the output waveform is under 10 ns, the response time is under 100 ns, and the maximum detection distance is 22 m. Further, experimental results demonstrate the superiority of omnidirectional sensors over planar ones in complex environments, successfully receiving 360° laser signals. The positional accuracy of the common point to be measured on the top of the omnidirectional sensor is confirmed to exceed 0.05 mm, and the accuracy of the angle of attitude exceeds 0.04°. Using the laser tracker, the measurement accuracy of the system is verified to be better than 0.3 mm. When rotating in the horizontal and pitch directions, the measurement accuracy is better than 0.35 mm and 0.47 mm, respectively, fulfilling the sub-millimeter precision requirement and expanding the application scope of distributed laser measurement systems.

Aiming at the distributed measurement system multi-equipment coordinated measurement, the data measured by each transmitting station are unified under a common coordinate system, which has the problems of complicated operation and high time and cost. In this paper, a fast calibration method for distributed measurement systems based on standard devices is proposed. The method designs a new type of architectural calibration board with non-coplanar measurement nodes, calculates the coordinates of the measurement nodes using geometric relations, solves the initial values of the system parameters, and substitutes the initial values into the L-M least squares algorithm for iterative optimization so that it only needs to carry out one scanning measurement for each reference position in the calibration process to complete the calibration. We designed an experiment in which a distributed measurement system consisting of four transmitting stations measured three standard rods (1650.073, 1156.704, and 875.950 mm) sequentially after calibration in a measuring space of 5 × 5 × 3 m3. The experimental results show that under the premise of ensuring that the measurement accuracy is better than 0.20 mm, the calibration of the parameters of the distributed measurement system can be completed within 20 min.

An accurate large-scale positioning system is a high-precision distributed large-scale dimensional metrology system that is widely used in smart manufacturing and assembly applications. The system comprises multiple transmitters with locations and orientations that are typically calibrated based on auxiliary measurement equipment. However, the calibration method is complicated and time consuming. We propose a rapid and flexible calibration method based on the highly precise three-dimensional coordinate control network established by a calibration bar with an accuracy of ± 0.005 mm, including both estimation and optimization algorithms. Exploratory experiments were performed to validate the feasibility of the proposed calibration method. The results demonstrate that the proposed calibration method enables the repeated measurement errors to be maintained at <0.05 mm in X and Y and <0.04 mm in Z. The standard deviation after calibration was 0.18 mm in comparison with the laser tracker.