scholarly journals Defect Detection in Striped Images Using a One-Dimensional Median Filter

2020 ◽  
Vol 10 (3) ◽  
pp. 1012
Author(s):  
Wei-Chen Lee ◽  
Pei-Ling Tai
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Median Filter ◽  
Detection Methods ◽  
Manufacturing Processes ◽  
Image Binarization ◽  
Image Processing Algorithm ◽  
One Dimensional ◽  
Modern Manufacturing ◽  
The Difference

Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.

Image Processing Algorithm for Apple Defect Detection

1989 ◽  
Vol 32 (1) ◽  
pp. 0267-0272 ◽  
Author(s):  
Gerald E. Rehkugler ◽  
James A. Throopmann
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Processing Algorithm ◽  
Image Processing Algorithm

scholarly journals A Comparison of Northern Hemisphere Atmospheric Rivers Detected by a New Image-Processing Based Method and Magnitude-Thresholding Based Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 628 ◽  
Author(s):  
Guangzhi Xu ◽  
Xiaohui Ma ◽  
Ping Chang ◽  
Lin Wang
Keyword(s):  
Image Processing ◽  
North Atlantic ◽  
North Pacific ◽  
Detection Method ◽  
New Method ◽  
Detection Methods ◽  
Image Processing Algorithm ◽  
Atmospheric Rivers ◽  
The North ◽  
The North Pacific

A majority of the existing atmospheric rivers (ARs) detection methods is based on magnitude thresholding on either the integrated water vapor (IWV) or integrated vapor transport (IVT). One disadvantage of such an approach is that the predetermined threshold does not have the flexibility to adjust to the fast changing conditions where ARs are embedded. To address this issue, a new AR detection method is derived from an image-processing algorithm that makes the detection independent of AR magnitude. In this study, we compare the North Pacific and Atlantic ARs tracked by the new detection method and two widely used magnitude thresholding methods in the present day climate. The results show considerable sensitivities of the detected AR number, shape, intensities and their accounted IVT accumulations to different methods. In many aspects, ARs detected by the new method lie between those from the two magnitude thresholding methods, but stand out with a greater number of AR tracks, longer track durations, and stronger AR-related moisture transport in the AR tracks. North Pacific and North Atlantic ARs identified by the new method account for around 100–120 ×   10 3 kg/m/s IVT within the AR track regions, about 50 % more than the other two methods. This is primarily due to the fact that the new method captures the strong IVT signals more effectively.

scholarly journals Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Median Filter ◽  
Area Under The Curve ◽  
Detection Methods ◽  
Flower Pollination Algorithm ◽  
Good Prediction ◽  
Concrete Wall ◽  
Promising Alternative

Crack detection is a crucial task in the periodic survey of high-rise buildings and infrastructure. Manual survey is notorious for low productivity. This study is aimed at establishing an image processing-based method for detecting cracks on concrete wall surfaces in an automatic manner. The Roberts, Prewitt, Canny, and Sobel algorithms are employed as the edge detection methods for revealing the crack textures appearing in concrete walls. The median filtering and object cleaning operations are used to enhance the image and facilitate the crack recognition outcome. Since the edge detectors, the median filter, and the object cleaning operation all require the appropriate selection of tuning parameters, this study relies on the differential flower pollination algorithm as a metaheuristic to optimize the image processing-based crack detection model. Experimental results point out that the newly constructed approach that employs the Prewitt algorithm can achieve a good prediction outcome with classification accuracy rate = 89.95% and area under the curve = 0.90. Therefore, the proposed metaheuristic optimized image processing approach can be a promising alternative for automatic recognition of cracks on the concrete wall surface.

scholarly journals Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1731
Author(s):  
Honggui Deng ◽  
Yu Cheng ◽  
Yuxin Feng ◽  
Junjiang Xiang
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Defect Detection ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Median Filter ◽  
Image Data ◽  
Activation Function ◽  
Image Processing Algorithm ◽  
Image Defect

Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

scholarly journals A Public Fabric Database for Defect Detection Methods and Results

2019 ◽  
Vol 19 (4) ◽  
pp. 363-374 ◽  
Author(s):  
Javier Silvestre-Blanes ◽  
Teresa Albero-Albero ◽  
Ignacio Miralles ◽  
Rubén Pérez-Llorens ◽  
Jorge Moreno
Keyword(s):  
Image Processing ◽  
Comparative Study ◽  
Defect Detection ◽  
Detection Methods ◽  
Classification Methods ◽  
New Methods ◽  
New Approaches ◽  
Private Collection

Abstract The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.

scholarly journals Image-Based Crack Detection Methods: A Review

2021 ◽  
Vol 6 (8) ◽  
pp. 115
Author(s):  
Hafiz Suliman Munawar ◽  
Ahmed W. A. Hammad ◽  
Assed Haddad ◽  
Carlos Alberto Pereira Soares ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Defect Detection ◽  
Visual Inspection ◽  
Crack Detection ◽  
Performance Outcomes ◽  
Detection Methods ◽  
Detection Techniques ◽  
Scanned Images ◽  
Infrastructure Maintenance

Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

Sign in / Sign up

Export Citation Format

Share Document