Manual inspection in printed circuit board manufacturing is highly susceptible to failure through human error. This opens the way for automated visual inspection. Several methods exist for detection based on images captured by a camera. The objective of this work is to develop a computer vision system using convolutional neural networks and classical computer vision techniques for locating soldering faults on printed circuit boards with through-hole technology. For this purpose, the OpenCV library on Python is used to detect the region of interest within the image prior to the analysis and classification of the convolutional neural network ResNET50. Two types of faults were presented as lack of solder and solder bridge. The results obtained in the experimental classification tests have an accuracy margin higher than 90%. This makes a viable use of automated visual inspection in the testing and inspection processes of errors in the soldering of printed circuit boards. The dataset is available at: https://github.com/asrf001/DatasetPCB.git.
|Title of host publication||Trends in Artificial Intelligence and Computer Engineering - Proceedings of ICAETT 2021|
|Editors||Miguel Botto-Tobar, Omar S. Gómez, Raul Rosero Miranda, Angela Díaz Cadena, Sergio Montes León, Washington Luna-Encalada|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|State||Published - 2022|
|Event||3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021 - Riobamba, Ecuador|
Duration: 10 Nov 2021 → 12 Nov 2021
|Name||Lecture Notes in Networks and Systems|
|Conference||3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021|
|Period||10/11/21 → 12/11/21|
Bibliographical noteFunding Information:
This work has been supported by the GIIAR research group and the Universidad Polit?cnica Salesiana.
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Automated visual inspection
- Classical computer vision
- Convolutional neural networks