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<citation_list><citation key="ref0"><unstructured_citation>M.H. Thigale, Shivani Gaikwad, Priyanka Nangare, Nivedita Hule. 1996. &quot; Defect detection and classification of PCB using MATLAB &quot;. International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 02 | Feb -2017.</unstructured_citation></citation><citation key="ref1"><unstructured_citation>Neelum Dave, Vikas Tambade, Balaji Pandhare, &quot; PCB Defect Detection Using Image Processing and Embedded System &quot;, International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 05 | May-2016.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>Bing Hu and Jianhui Wang, &quot; Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network &quot;. IEEE Access May 24, 2020.</unstructured_citation></citation><citation key="ref3"><doi>10.1109/ACCESS.2022.3228206</doi><unstructured_citation>Wei Chen, Zhongtian Huang, Qian Mu, And Yi Sun, &quot; PCB Defect Detection Method Based on Transformer-YOLO &quot;, IEEE Access November 19 2022. https://doi.org/10.1109/ACCESS.2022.3228206</unstructured_citation></citation><citation key="ref4"><doi>10.1109/ACCESS.2023.3245093</doi><unstructured_citation>Qin Ling And Nor Ashidi Mat Isa, &quot;Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning &quot;, IEEE Access February 8, 2023https://doi.org/10.1109/ACCESS.2023.3245093</unstructured_citation></citation><citation key="ref5"><doi>10.1109/ACCESS.2022.3214306</doi><unstructured_citation>Zheng, Jianfeng &amp; Sun, Xiaopeng &amp; Zhou, Haixiang &amp; Tian, Chenyang &amp; Qiang, Hao. (2022). Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks. IEEE Access. PP. 1-1. 10.1109/ACCESS.2022.3214306. https://doi.org/10.1109/ACCESS.2022.3214306</unstructured_citation></citation><citation key="ref6"><doi>10.54097/fcis.v6i2.01</doi><unstructured_citation>Liu, Chang &amp; Zhou, Xiangyang &amp; Li, Jun &amp; Ran, Chuantao. (2023). PCB Board Defect Detection Method based on Improved YOLOv8. Frontiers in Computing and Intelligent Systems. 6. 1-6. 10.54097/fcis.v6i2.01. https://doi.org/10.54097/fcis.v6i2.01</unstructured_citation></citation><citation key="ref7"><doi>10.3390/electronics12092120</doi><unstructured_citation>Yang, Yujie, and Haiyan Kang. 2023. &quot;An Enhanced Detection Method of PCB Defect Based on Improved YOLOv7&quot; Electronics 12, no. 9: 2120. https://doi.org/10.3390/electronics12092120.</unstructured_citation></citation><citation key="ref8"><doi>10.32604/cmc.2023.046376</doi><unstructured_citation>X. Hu, D. Kong, X. Liu, J. Zhang, and D. Zhang &quot;Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism,&quot; Comput. Mater. Contin., vol.78,no.1,pp.915-933.2024.https://doi.org/10.32604/cmc.2023.046376 https://doi.org/10.32604/cmc.2023.046376</unstructured_citation></citation><citation key="ref9"><doi>10.1016/j.engappai.2023.106359</doi><unstructured_citation>Wujin Jiang, Taifu Li, Shaolin Zhang, Wenbin Chen, Jie Yang, PCB defects target detection combining multi-scale and attention mechanism, Engineering Applications of Artificial Intelligence,Volume 123, Part C, 2023,106359, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2023.106359.</unstructured_citation></citation><citation key="ref10"><doi>10.54105/ijipr.H9682.083523</doi><unstructured_citation>Singh, B. P., &amp; Barik, R. (2023). Image Segmentation Based Automated Skin Cancer Detection Technique. In Indian Journal of Image Processing and Recognition (Vol. 3, Issue 5, pp. 1-6). https://doi.org/10.54105/ijipr.h9682.083523</unstructured_citation></citation><citation key="ref11"><doi>10.35940/ijeat.E9309.069520</doi><unstructured_citation>Mirra, K. B., Pooja, P., Ranchani, S., &amp; kumari, R. R. (2020). Fruit Quality Analysis using Image Processing. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 88-91). https://doi.org/10.35940/ijeat.e9309.069520</unstructured_citation></citation><citation key="ref12"><doi>10.35940/ijitee.J9184.0881019</doi><unstructured_citation>Patil, Miss. M. B., &amp; Phakade, Prof. S. V. (2019). Adaptive Head Light System using Image Processing. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 1178-1180). https://doi.org/10.35940/ijitee.j9184.0881019</unstructured_citation></citation><citation key="ref13"><doi>10.35940/ijrte.C5677.098319</doi><unstructured_citation>P, Raju., Rao.V, M., &amp; Rao.B, P. (2019). An Efficient Optimized Probabilistic Neural Network Based Kidney Stone Detection and Segmentation over Ultrasound Images. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 7465-7473). https://doi.org/10.35940/ijrte.c5677.098319</unstructured_citation></citation><citation key="ref14"><doi>10.35940/ijies.H0958.025820</doi><unstructured_citation>A., O., &amp; O, B. (2020). An Iris Recognition and Detection System Implementation. In International Journal of Inventive Engineering and Sciences (Vol. 5, Issue 8, pp. 8-10). https://doi.org/10.35940/ijies.h0958.025820</unstructured_citation></citation></citation_list>
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