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M Clark
2016-10-02 10:08:21 +08:00
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@@ -25,10 +25,12 @@ For data augmentation, we used a range of flips, affine transforms, and jitter.
### Model
![model_diagram](images/model_diagram.png)
*The model architecture. Every box is an inception module or convolution with the number of feature layers denoted in brackets. The output size is listed below each box and the coloured arrows denote different operations.*
![inception_module](images/inception_module.png)
*The inception module used in this paper, as originally proposed in [[5]](#5).*
Our model is a convolutional autoencoder much like the U-Net architecture [[3]](#3)[[4]](#4) but with inception modules instead of convolution blocks. The inception modules are those originally proposed in [[5]](#5) but with asymmetric convolutions. For example, a 3x3 convolution is replaced with a 3x1 convolution, then batch normalisation, then a 1x3 convolution. This approach gives similar results with fewer parameters. All weights were initialised using a scaled Gaussian distribution [[6]](#6). All convolution blocks were followed by batch normalisation then activation [[7]](#7). The activation used was leaky rectified linear units (LReLU) with a slope of -0.1x for inputs below zero.
@@ -52,7 +54,7 @@ The final results gave an accuracy of 0.73 for the test data set and 0.745 for t
The image below shows results and displays the weaknesses of this model. Firstly, it fails where foliage obscures large parts of the pipe, and secondly, it occasionally returns false positives for bleached wood or similar objects. This may be overcome with further training data or by using a region proposal network to evaluate pipe shaped regions.
|results|training|testing|
|-|-|-|
|-------|--------|-------|
|**accuracy**|0.745|0.73|
|**dice loss**|0.079|0.100|
|**mathews correlation coeffecient**|0.82|0.77|
@@ -60,6 +62,7 @@ The image below shows results and displays the weaknesses of this model. Firstly
*Results on unaugmented training and test data*
![results](images/results4.png)
*Results for test data. Columns: (a) the input data, (b) the ground truth mask (c) the mask predicted by this model.*