Research Status of Local Defect Detection Technology of Ultraviolet Image Intensifier Field of View
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摘要: 紫外像增强器是一种对紫外辐射敏感的成像器件，视场瑕疵是其成像效果的主要制约因素。目前，视场瑕疵检测技术主要分为人工和机器视觉两种方法。本文首先阐述了视场瑕疵的定义和检测标准。接着从瑕疵交叠靠近、大小和数量特性的角度，分析了视场瑕疵检测的难点。随后，重点介绍了紫外像增强器视场瑕疵检测技术的研究现状。结合当前的检测需求和不足，调研了深度学习技术在其他领域的瑕疵检测效果。最后，从理论上进行了可行性分析，并提出了基于深度学习视场瑕疵检测的思路，旨在为紫外像增强器视场瑕疵检测提供一种新的解决方案，推动其向着更加实用、智能化的方向发展。Abstract: Ultraviolet image intensifiers are imaging devices that are sensitive to ultraviolet radiation. Defects in the field of view are the main factors restricting the imaging effect of ultraviolet image intensifiers. Currently, the field-of-view defect detection technology is mainly divided into artificial and machine vision. This paper explains the definitions and detection standards for field defects. Subsequently, the difficulties in field defect detection are analyzed from the perspectives of defect-overlapping proximity, size, and quantity. Next, the research status of the field-of-view defect detection technology of ultraviolet image intensifiers is introduced. Combined with the current detection requirements and deficiencies, the defect detection effect of deep-learning technology in other fields was investigated. Finally, a theoretical feasibility analysis is presented, and the concept of field defect detection based on deep learning is proposed. The purpose is to provide a new solution for field defect detection of ultraviolet image intensifiers and promote their development in a practical and intelligent direction.
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Keywords:
- image intensifier /
- flaw detection in field of view /
- machine vision /
- deep learning
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图 1 采集的视场图像中典型瑕疵示例
Figure 1. Typical defect examples in the field of view image acquisition
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图 3 视场中小瑕疵数量多、分布杂乱
Figure 3. The number of small defects in the field of view is large and the distribution is messy
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图 6 基于简化版Robert算法的视场瑕疵检测
Figure 6. Defect detection based on simplified Robert algorithm
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图 8 在荧光屏中人为引入1个瑕疵的测试结果
Figure 8. The test results of intentionally introducing one defect on the fluorescent screen
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图 9 海康威视Vision Master算法平台的检测效果：(a) 表面亮斑；(b) 表面划痕；(c) 形状异常；(d) 轮廓残缺；(e) 划痕检测；(f) 字符缺陷；(g) 崩边检测；(h) 污渍检测；(e) 划痕检测；(f) 字符缺陷；(g) 崩边检测；(h) 污渍检测
Figure 9. The detection results of Hikvision's Vision Master algorithm platform: (a) Surface speck; (b) Surface scratch; (c) Shape anomaly; (d) Contour incomplete; (e) Scratch detection; (f) Character defect; (g) Edge collapse detection; (h) Stain detection
表 1 视场中各分区允许不同大小暗点存在的数量
Table 1 The allowed number of scotoma of different sizes in each zone of the FOV
Size of the scotoma /mm Different zones of the FOV ϕ5.6 mm ϕ5.6 mm to ϕ14.7 mm ϕ14.7 mm to ϕ18 mm ≥0.457 0 0 0 0.381 to 0.457 0 0 2 0.305 to 0.381 0 5 8 0.229 to 0.305 1 9 23 0.152 to 0.229 3 35 35 ≤0.152 Sparsely scattered and can be ignored 
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表 2 样本中各类型瑕疵数量占比情况
Table 2 The proportion of each type of defect in the samples
Defect types Scotoma Bright spot Macula Speck Stripe Number 2692 369 435 172 481 Percentage 64.98% 8.89% 10.48% 4.15% 11.59%
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表 3 不同机器视觉视场瑕疵检测方法的对比
Table 3 Comparison of different machine vision field of view defect detection methods
Detection algorithm Key technical features Advantages Limitation Literature reference Threshold segmentation-based algorithm Using a fixed threshold method Compared to manual detection, it improves the efficiency of field defect detection Prone to interference from external factors, requiring manual assistance in discrimination Reference [4] Employing a multi-region thresholding method Further enhancing the detection accuracy of field defects Prone to interference from external factors, requiring manual assistance in discrimination Reference [5] Manually adjusting the threshold based on the actual field conditions Designed two detection modes, 'full-screen' and 'half-screen', to meet different detection needs The detection speed of defects is relatively slow Reference [6] Edge-based segmentation algorithm Employed a simplified Robert edge operator Simpler and faster defect detection The detection performance is not satisfactory for complex and irregular field defects Reference [7] Utilized the Canny edge operator More accurate localization with the design of an automatic method for selecting specific detection areas, thereby improving defect detection speed There is a certain deviation in selecting the detection area, leading to the omission of defects along the edges of the region Reference [8] Based on signal-to-noise ratio (SNR) theory Utilizing spatiotemporal signal-to-noise ratio differences in the field defect regions High detection accuracy and not limited by the shape of defects Prone to interference from external factors, unable to determine the shape and size of field defects Reference [9]
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表 4 人工检测与机器视觉检测的对比
Table 4 Comparison between manual detection and machine vision detection
Detection method Advantages Shortcomings Detection accuracy Detection speed Manual inspection The method is simple, with relatively good reliability, and flexible operation Low detection efficiency, with relatively high labor costs Relatively high Relatively low Machine vision inspection Fast detection efficiency and speed, with excellent detection results For the detection of complex defects, the performance is not satisfactory, and manual intervention in testing is required Moderate Moderate
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表 5 基于深度学习的瑕疵检测的部分研究应用
Table 5 Some research applications of defect detection based on deep learning
Detection method Inspected object Experimental results Literature reference Convolutional neural network (CNN) Currency note image The defect recognition accuracy is 95.6% Reference[13] Cold-rolled steel plate The model achieves a defect detection accuracy of 93% Reference [14] Fabric Defect classification accuracy is over 95% Reference [15] Solar panel Defect detection accuracy is above 88.42% Reference [16] Fully convolutional network (FCN) Crack Addressing the issue of local information loss in detection Reference [17] Concrete The recognition accuracy of surface crack defects can reach 90% Reference [18] TFT-LCD Accurate positioning and recognition of conductive particles can be achieved Reference [19] Steel The classification accuracy of defects is above 91.6% Reference [20] Auto encoder (AE) Fabric The accuracy of defect detection is consistently above 98.75% Reference [21] Rail Achieved excellent defect detection results Reference [22] Steel The defect classification rate is improved by about 16% compared to traditional methods Reference [23] Residual network(ResNet) Corn leaf blade The accuracy of identifying diseases and pests can reach 98.5% Reference [24] Photovoltaic panel The accuracy of recognizing ash accumulation level is 90.7% Reference [25] Deep belief network(DBN) Wooden board Outperforms traditional CNN detection methods in performance Reference [26] Cable tunnel More accurate and versatile compared to existing algorithms Reference [27] Metal Low scratch omission rate, better detection performance Reference [28] Recurrent neural network(RNN) Mobile phone screen The average accuracy for samples with complex sizes and shapes is 90.36% Reference [29]
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