Recyclable fluorescence sensing based on copper clusters for simultaneous determination of copper ions and ammonia.

A one-step strategy for synthesizing fluorescent copper clusters stabilized by L-cysteine has been successfully established in aqueous solutions. The direct determination of copper ions was realized by the fluorescence enhancement phenomenon caused by the preparation and aggregation process. At the same time, ammonia treatment can lead to rapid fluorescence quenching, resulting from the influence on the aggregation behavior of Cu clusters, while the fluorescence can be recovered by the continuous addition of copper ions. Therefore, a recyclable fluorescence sensing system is constructed for the simultaneous determination of copper ions and ammonia. This method is simple, anti-interference and has been successfully applied to the determination of environmental samples.

Real-time batch inspection system for surface defects on circular optical filters.

Optical filters, one of the essential parts of many optical instruments, are used to select a specific radiation band of optical devices. There are specifications for the surface quality of the optical filter in order to ensure the instrument's regular operation. The traditional machine learning techniques for examining the optical filter surface quality mentioned in the current studies primarily rely on the manual extraction of feature data, which restricts their ability to detect optical filter surfaces with multiple defects. In order to solve the problems of low detection efficiency and poor detection accuracy caused by defects too minor and too numerous types of defects, this paper proposes a real-time batch optical filter surface quality inspection method based on deep learning and image processing techniques. The first part proposes an optical filter surface defect detection and identification method for seven typical defects. A deep learning model is trained for defect detection and recognition by constructing a dataset. The second part uses image processing techniques to locate the accurate position of the defect, determine whether the defect is located within the effective aperture, and analyze the critical eigenvalue data of the defect. The experimental results show that the method improves productivity and product quality and reduces the manual workload by 90%. The proposed model and method also compare the results of surface defect detection with the actual measurement data in the field, verifying that the method has good recognition accuracy while improving efficiency.

Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation.

The tendency to increase the accuracy and quality of optical parts inspection can be observed all over the world. The imperfection of manufacturing techniques can cause different defects on the optical component surface, making surface defects inspection a crucial part of the manufacturing of optical components. Currently, the inspection of lenses, filters, mirrors, and other optical components is performed by human inspectors. However, human-based inspections are time-consuming, subjective, and incompatible with a highly efficient high-quality digital workflow. Moreover, they cannot meet the complex criteria of ISO 10110-7 for the quality pass and fail optical element samples. To meet the high demand for high-quality products, intelligent visual inspection systems are being used in many manufacturing processes. Automated surface imperfection detection based on machine learning has become a fascinating and promising area of research, with a great direct impact on different visual inspection applications. In this paper, an optical inspection platform combining parallel deep learning-based image-processing approaches with a high-resolution optomechanical module was developed to detect surface defects on optical plane components. The system involves the mechanical modules, the illumination and imaging modules, and the machine vision algorithm. Dark-field images were acquired using a 2448×2048-pixel line-scanning CMOS camera with 3.45 µm per-pixel resolution. Convolutional neural networks and semantic segmentation were used for a machine vision algorithm to detect and classify defects on captured images of optical bandpass filters. The experimental results on different bandpass filter samples have shown the best performance compared to traditional methods by reaching an impressive detection speed of 0.07 s per image and an overall detection pixel accuracy of 0.923.