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Pregled bibliografske jedinice broj: 1249823

Detection and Classification of Printed Circuit Boards Using YOLO Algorithm


Glučina, Matko; Anđelić, Nikola; Lorencin, Ivan; Car, Zlatan
Detection and Classification of Printed Circuit Boards Using YOLO Algorithm // Electronics (Basel), 12 (2023), 3; 667, 21 doi:10.3390/electronics12030667 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1249823 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Detection and Classification of Printed Circuit Boards Using YOLO Algorithm

Autori
Glučina, Matko ; Anđelić, Nikola ; Lorencin, Ivan ; Car, Zlatan

Izvornik
Electronics (Basel) (2079-9292) 12 (2023), 3; 667, 21

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
classification ; detection ; PCB ; semantic segmentation ; YOLOv5

Sažetak
Printed circuit boards (PCBs) are an indispensable part of every electronic device used today. With its computing power, it performs tasks in much smaller dimensions, but the process of making and sorting PCBs can be a challenge in PCB factories. One of the main challenges in factories that use robotic manipulators for “pick and place” tasks are object orientation because the robotic manipulator can misread the orientation of the object and thereby grasp it incorrectly, and for this reason, object segmentation is the ideal solution for the given problem. In this research, the performance, memory size, and prediction of the YOLO version 5 (YOLOv5) semantic segmentation algorithm are tested for the needs of detection, classification, and segmentation of PCB microcontrollers. YOLOv5 was trained on 13 classes of PCB images from a publicly available dataset that was modified and consists of 1300 images. The training was performed using different structures of YOLOv5 neural networks, while nano, small, medium, and large neural networks were used to select the optimal network for the given challenge. Additionally, the total dataset was cross validated using 5-fold cross validation and evaluated using mean average precision, precision, recall, and F1-score classification metrics. The results showed that large, computationally demanding neural networks are not required for the given challenge, as demonstrated by the YOLOv5 small model with the obtained mAP, precision, recall, and F1-score in the amounts of 0.994, 0.996, 0.995, and 0.996, respectively. Based on the obtained evaluation metrics and prediction results, the obtained model can be implemented in factories for PCB sorting applications.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Strojarstvo, Temeljne tehničke znanosti



POVEZANOST RADA


Projekti:
--uniri-mladi-technic-22-57 - Razvoj inteligentnog sustava za estimaciju točke maksimalne snage fotonaponskog sustava s primjenom na autonomna plovila (Lorencin, Ivan) ( CroRIS)
--uniri-mladi-technic-22-61 - Energetska optimizacija industrijskih robotskih manipulatora primjenom algoritama evolucijskog računarstva (Anđelić, Nikola) ( CroRIS)

Profili:

Avatar Url Zlatan Car (autor)

Avatar Url Nikola Anđelić (autor)

Avatar Url Ivan Lorencin (autor)

Avatar Url Matko Glučina (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Glučina, Matko; Anđelić, Nikola; Lorencin, Ivan; Car, Zlatan
Detection and Classification of Printed Circuit Boards Using YOLO Algorithm // Electronics (Basel), 12 (2023), 3; 667, 21 doi:10.3390/electronics12030667 (međunarodna recenzija, članak, znanstveni)
Glučina, M., Anđelić, N., Lorencin, I. & Car, Z. (2023) Detection and Classification of Printed Circuit Boards Using YOLO Algorithm. Electronics (Basel), 12 (3), 667, 21 doi:10.3390/electronics12030667.
@article{article, author = {Glu\v{c}ina, Matko and An\djeli\'{c}, Nikola and Lorencin, Ivan and Car, Zlatan}, year = {2023}, pages = {21}, DOI = {10.3390/electronics12030667}, chapter = {667}, keywords = {classification, detection, PCB, semantic segmentation, YOLOv5}, journal = {Electronics (Basel)}, doi = {10.3390/electronics12030667}, volume = {12}, number = {3}, issn = {2079-9292}, title = {Detection and Classification of Printed Circuit Boards Using YOLO Algorithm}, keyword = {classification, detection, PCB, semantic segmentation, YOLOv5}, chapternumber = {667} }
@article{article, author = {Glu\v{c}ina, Matko and An\djeli\'{c}, Nikola and Lorencin, Ivan and Car, Zlatan}, year = {2023}, pages = {21}, DOI = {10.3390/electronics12030667}, chapter = {667}, keywords = {classification, detection, PCB, semantic segmentation, YOLOv5}, journal = {Electronics (Basel)}, doi = {10.3390/electronics12030667}, volume = {12}, number = {3}, issn = {2079-9292}, title = {Detection and Classification of Printed Circuit Boards Using YOLO Algorithm}, keyword = {classification, detection, PCB, semantic segmentation, YOLOv5}, chapternumber = {667} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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