Automated optical inspection of surface mount components using 2D machine vision

Author(s):  
Y. Watanabe
2021 ◽  
Vol 11 (13) ◽  
pp. 6017
Author(s):  
Gerivan Santos Junior ◽  
Janderson Ferreira ◽  
Cristian Millán-Arias ◽  
Ramiro Daniel ◽  
Alberto Casado Junior ◽  
...  

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.


2015 ◽  
Vol 82 (5) ◽  
Author(s):  
Max-Gerd Retzlaff ◽  
Josua Stabenow ◽  
Jürgen Beyerer ◽  
Carsten Dachsbacher

AbstractWhen designing or improving systems for automated optical inspection (AOI), systematic evaluation is an important but costly necessity to achieve and ensure high quality. Computer graphics methods can be used to quickly create large virtual sets of samples of test objects and to simulate image acquisition setups. We use procedural modeling techniques to generate virtual objects with varying appearance and properties, mimicking real objects and sample sets. Physical simulation of rigid bodies is deployed to simulate the placement of virtual objects, and using physically-based rendering techniques we create synthetic images. These are used as input to an AOI system instead of physically acquired images. This enables the development, optimization, and evaluation of the image processing and classification steps of an AOI system independently of a physical realization. We demonstrate this approach for shards of glass, as sorting glass is one challenging practical application for AOI.


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