Deep learning for ground penetrating radar image processing
This project focus on finding the relationship between 2D B-scan images and 3D B-scan images generated by gprMax for convolutional neural network training purpose in object detection and classification. Cylinders with different material and orientations have also been discussed to find the effects o...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/140154 |
| الوسوم: |
إضافة وسم
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| الملخص: | This project focus on finding the relationship between 2D B-scan images and 3D B-scan images generated by gprMax for convolutional neural network training purpose in object detection and classification. Cylinders with different material and orientations have also been discussed to find the effects on the accuracy of 2D B-scan modelling. Results show that 2D B-scan images are very similar to that of 3D B-scan images of cylinders. In terms of shape and relative position of hyperbola, 2D B-scan images are exactly the same as 3D B-scan images. The minor difference in colour intensity can be negligible. The change in orientation of cylinders can also be represented in 2D B-scan images. |
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