Image sensing method and defect detection algorithm for a 256-Mb and 1-Gb DRAM mask inspection system

1997 ◽  
Author(s):  
Hiromu Inoue ◽  
Kentaro Okuda ◽  
Takehiko Nomura ◽  
Hideo Tsuchiya ◽  
Mitsuo Tabata
Keyword(s):  
Defect Detection ◽  
Detection Algorithm ◽  
Inspection System ◽  
Image Sensing ◽  
Mask Inspection

Research and Achievement on Cigarette Label Printing Defect Detection Algorithm

2012 ◽  
Vol 200 ◽  
pp. 689-693
Author(s):  
Meng Xiao Li ◽  
Quan Xiang Liu ◽  
Cong You He
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Analytical Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Defect Analysis ◽  
Image Sensing ◽  
Detection Technology ◽  
Image Difference

The cigarette label printing defect detection algorithm mainly includes image sensing and defect analysis. This paper carried out a deep research and analysis on the cigarette label printing defect detection algorithm by combing the domestic and foreign advanced machine vision-based printing defect detection technology, and improved the existing defect detection algorithm. Besides, it proposed to apply the minimum external rectangle in analyzing defect shape, realized the functions of detecting defect of printing images as well as analyzing and displaying the defect, and proved that the combination application of image difference and minimum external rectangle analytical method can bring better timeliness and higher detection accuracy.

scholarly journals EfficientDet for fabric defect detection based on edge computing

2021 ◽  
Vol 16 ◽  
pp. 155892502110083
Author(s):  
Shaojun Song ◽  
Junfeng Jing ◽  
Yanqing Huang ◽  
Mingyang Shi
Keyword(s):  
Cloud Computing ◽  
Defect Detection ◽  
Textile Industry ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Detection Methods ◽  
Inspection System ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Manual Methods

The productivity of textile industry is positively correlated with the efficiency of fabric defect detection. Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods. Nonetheless, these cloud computing-based methods are still suboptimal due to the data transmission latency between the end devices and the cloud. To facilitate defect detection with more efficiency, a low-latency, low power consumption, easy upgrade, and automatical visual inspection system with the help of edge computing are proposed in this work. Firstly, the method uses EfficientDet-D0 as the detection algorithm, integrating the advantages of lightweight and scalable and can suit the resource-constrained edge device. Secondly, we performed data augmentations on five fabric datasets and verified the adaptability of the model in different types of fabrics. Finally, we transplanted the trained model to the edge device NVIDIA Jetson TX2 and optimized the model with TensorRT to make it detection faster. The performance of the proposed method is evaluated in five fabric datasets. The detection speed is up to 22.7 frame per second (FPS) on the edge device Jetson TX2. Compared with the cloud-based method, the response time is reduced by 2.5 times, with the capability of real-time industrial defect detection.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

Application of the SEMSpec electron-beam inspection system to in-process defect detection on semiconductor wafers

1996 ◽  
Vol 30 (1-4) ◽  
pp. 567-570 ◽  
Author(s):  
T.R. Cass ◽  
D. Hendricks ◽  
J. Jau ◽  
H.J. Dohse ◽  
A.D. Brodie ◽  
...  
Keyword(s):  
Electron Beam ◽  
Defect Detection ◽  
Inspection System

scholarly journals Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5136
Author(s):  
Xiaoxin Fang ◽  
Qiwu Luo ◽  
Bingxing Zhou ◽  
Congcong Li ◽  
Lu Tian
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Three Dimensional ◽  
Image Features ◽  
Stereoscopic Vision ◽  
Research Progress ◽  
Inspection System ◽  
Two Dimensional ◽  
Surface Defect Detection ◽  
Planar Materials

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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