Comprehensive defect-detection method for a small-sized curved optical lens

Pan, Jinda; Yan, Ning; Zhu, Linlin; Zhang, Xiaodong

doi:10.1364/ao.59.000234

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…In order to improve the defect detection accuracy of small size curved optical lenses, Pan, J.D. et al [9] proposed a comprehensive defect detection system based on transmission streak deflection method, dark field illumination and light transmission, and the experimental results show that the proposed system can be applied to the actual mass production of small size curved optical lenses. For defect detection in electronic screens, Gao Yan et al [10] designed an image processing-based screen defect detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Zhang

et al. 2022

Electronics

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For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision mAP@0.5 of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection.

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Section: Related Workmentioning

confidence: 99%

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Zhang

et al. 2022

Electronics

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Automatic identification and classification of surface defects in small-sized optics

Zhang,

Yi,

Kong

et al. 2024

Meas. Sci. Technol.

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Precision optics are widely used in lighting systems, imaging systems and high precision inspection equipment. The presence of surface defects in optical components can seriously affect the design performance of the equipment, so the optical components must be rigorously inspected to eliminate defective optical components. Currently, engineering products are moving toward smaller sizes, resulting in smaller optical elements. However, realizing small-sized optical elements is a great challenge because it requires micrometer-scale high-resolution image acquisition and automatic detection of defects on their surfaces. A machine learning-based surface defect detection method is proposed for a wide variety of surface defects and small sizes. A dark-field micro-scattering imaging system is used to acquire dark-field images. Then image segmentation and feature analysis are performed on each image to extract the original feature data and compressed with relifF algorithm. Based on the compressed feature data, a classification model is built using a support vector mechine(SVM). The detection method can classify three kinds of defects, namely, dig, scratch, and scuff mark, with an accuracy of up to 98%. The experimental results of this study show that the method can effectively and accurately detect several microdefects occurring in small-size optical components, thus providing valuable insights for the optimization of mass production parameters of optical components.

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Detection of weak micro-scratches on aspherical lenses using a Gabor neural network and transfer learning

2022

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Surface defect detection is a crucial step in ensuring the quality of lenses. One method to check for surface defects is to use an optical system integrated with an industrial camera to magnify and highlight the position of a defect on the surface of a lens. Therefore, automatic optical inspection systems are applied to detect micro-defects. In this study, we propose an automatic inspection platform based on a deep neural network for automatically imaging and examining the surface of a lens. High-resolution images of 2448 × 2048 pixels are acquired using a hybrid lighting system. A convolutional neural network integrated with a trainable Gabor filter is used as a machine vision algorithm to perform image classification and defect segmentation tasks. The experimental results show that the proposed method effectively performed with noise in the background, achieving a segmentation accuracy of 98%.

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Comprehensive defect-detection method for a small-sized curved optical lens

Cited by 6 publications

References 12 publications

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Automatic identification and classification of surface defects in small-sized optics

Detection of weak micro-scratches on aspherical lenses using a Gabor neural network and transfer learning

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