scholarly journals Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bangchao Liu ◽  
Youping Chen ◽  
Jingming Xie ◽  
Bing Chen
Keyword(s):  
Defect Detection ◽  
Large Scale ◽  
Feature Fusion ◽  
Detection System ◽  
Reference Image ◽  
Inspection System ◽  
Fusion Algorithm ◽  
Illumination Variation ◽  
Image Defect ◽  
Edge Feature

Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.

scholarly journals An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2636 ◽  
Author(s):  
Xia Fang ◽  
Wang Jie ◽  
Tao Feng
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Large Scale ◽  
Detection System ◽  
Scale Production ◽  
System Matrix ◽  
Rotating Platform ◽  
Limited Region ◽  
The Neural Network ◽  
Large Scale Production

In the field of machine vision defect detection for a micro workpiece, it is very important to make the neural network realize the integrity of the mask in analyte segmentation regions. In the process of the recognition of small workpieces, fatal defects are always contained in borderline areas that are difficult to demarcate. The non-maximum suppression (NMS) of intersection over union (IOU) will lose crucial texture information especially in the clutter and occlusion detection areas. In this paper, simple linear iterative clustering (SLIC) is used to augment the mask as well as calibrate the score of the mask. We propose an SLIC head of object instance segmentation in proposal regions (Mask R-CNN) containing a network block to learn the quality of the predict masks. It is found that parallel K-means in the limited region mechanism in the SLIC head improved the confidence of the mask score, in the context of our workpiece. A continuous fine-tune mechanism was utilized to continuously improve the model robustness in a large-scale production line. We established a detection system, which included an optical fiber locator, telecentric lens system, matrix stereoscopic light, a rotating platform, and a neural network with an SLIC head. The accuracy of defect detection is effectively improved for micro workpieces with clutter and borderline areas.

Automated defect detection system in MEMS manufacturing

2018 ◽  
Vol 11 (7-8) ◽  
pp. 542-548
Author(s):  
K. Raketov ◽  
N. Israilev ◽  
A. Kazachkov ◽  
E. Zablotskaya ◽  
I. Rod ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection System

Novel Feature Fusion Module Based Detector for Small Insulator Defect Detection

2021 ◽  
pp. 1-1
Author(s):  
Zishu Gao ◽  
Guodong Yang ◽  
En Li ◽  
Zize Liang
Keyword(s):  
Defect Detection ◽  
Feature Fusion

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

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