Image processing and deep learning based analysis of 3D X-ray PCB images
In this project, I propose a modified U-Net architecture for segmenting PCB (Printed Circuit Board) images. The proposed model consists of an encoder and a decoder structure with a connection of skip that enable integrations of low-level and high-level features for accurate segmentation. To enhance...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167086 |
| _version_ | 1826109581965656064 |
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| author | Zou, Haoxin |
| author2 | Gwee Bah Hwee |
| author_facet | Gwee Bah Hwee Zou, Haoxin |
| author_sort | Zou, Haoxin |
| collection | NTU |
| description | In this project, I propose a modified U-Net architecture for segmenting PCB (Printed Circuit Board) images. The proposed model consists of an encoder and a decoder structure with a connection of skip that enable integrations of low-level and high-level features for accurate segmentation. To enhance the segmentation performance, I introduce dilated convolutions, dense connections and convolutional layers in the decoder part. Additionally, we adopt a mixture of binary cross-entropy as well as dice loss functions to optimize the model during training. The intended model is assessed on the public dataset of PCB images.
Comparative analysis reveals that our model’s performance surpasses that of its competitors with an general segmentation accuracy of 94.2%. Furthermore, the proposed model is computationally efficient and can segment a PCB image in 1.52s. |
| first_indexed | 2024-10-01T02:20:35Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/167086 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:20:35Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1670862023-07-04T16:44:17Z Image processing and deep learning based analysis of 3D X-ray PCB images Zou, Haoxin Gwee Bah Hwee School of Electrical and Electronic Engineering ebhgwee@ntu.edu.sg Engineering::Electrical and electronic engineering In this project, I propose a modified U-Net architecture for segmenting PCB (Printed Circuit Board) images. The proposed model consists of an encoder and a decoder structure with a connection of skip that enable integrations of low-level and high-level features for accurate segmentation. To enhance the segmentation performance, I introduce dilated convolutions, dense connections and convolutional layers in the decoder part. Additionally, we adopt a mixture of binary cross-entropy as well as dice loss functions to optimize the model during training. The intended model is assessed on the public dataset of PCB images. Comparative analysis reveals that our model’s performance surpasses that of its competitors with an general segmentation accuracy of 94.2%. Furthermore, the proposed model is computationally efficient and can segment a PCB image in 1.52s. Master of Science (Electronics) 2023-05-15T06:46:24Z 2023-05-15T06:46:24Z 2023 Thesis-Master by Coursework Zou, H. (2023). Image processing and deep learning based analysis of 3D X-ray PCB images. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167086 https://hdl.handle.net/10356/167086 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Zou, Haoxin Image processing and deep learning based analysis of 3D X-ray PCB images |
| title | Image processing and deep learning based analysis of 3D X-ray PCB images |
| title_full | Image processing and deep learning based analysis of 3D X-ray PCB images |
| title_fullStr | Image processing and deep learning based analysis of 3D X-ray PCB images |
| title_full_unstemmed | Image processing and deep learning based analysis of 3D X-ray PCB images |
| title_short | Image processing and deep learning based analysis of 3D X-ray PCB images |
| title_sort | image processing and deep learning based analysis of 3d x ray pcb images |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167086 |
| work_keys_str_mv | AT zouhaoxin imageprocessinganddeeplearningbasedanalysisof3dxraypcbimages |