MobileNet-SSD for surface detection
In this Final Year Project (FYP), we aim to develop a Mobilenet-SSD network to detect defects in surface images. In particular, the SSD detection network locates objects in the feature map using a collection of default boxes of various aspect ratios and sizes. To handle different object sizes, th...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/165968 |
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| 總結: | In this Final Year Project (FYP), we aim to develop a Mobilenet-SSD network to
detect defects in surface images. In particular, the SSD detection network locates
objects in the feature map using a collection of default boxes of various aspect ratios
and sizes. To handle different object sizes, the SSD uses feature maps from different
resolutions and integrates all computing into a single network. However, the large
number of parameters makes the SSD too slow for edge devices. MobileNets, on the
other hand, have a streamlined architecture using depth-wise separable convolutions
to build lightweight deep neural networks. Therefore, we integrate MobileNet with
SSD to create a Mobilenet-SSD network for surface identification that is both fast
and accurate. Experimental results on the PASCAL VOC and NEU-DET datasets
have validated the effectiveness of our Mobilenet-SSD network. |
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