# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 2 Improved FraSegNet Algorithm  
### FraSegNet Algorithm  
FraSegNet was introduced in 2021 by Prof. [PERSON] and his team from Beijing Jiaotong University [27]. The algorithm is designed to extract nodal fissures in rocks with high precision. It improves segmentation accuracy by using a modern neural network and a new attention mechanism. FraSegNet uses a classic encoder-decoder structure. Compared to traditional networks, it performs better, especially in complex backgrounds and with fine details, by improving the feature channels' adaptability. The FraSegNet algorithm has five main parts: an input layer, an encoder, a jump connection, a decoder, and an output layer. The network structure is shown in Figure 1.  
Figure 1: FraSegNet network structure.  
Using an input image of 224 \(\times\) 224 pixels as an example, the process of recognizing and extracting target nodes with FraSegNet is described as follows:  
1. The input image is a 224 \(\times\) 224 grayscale rock image with nodal fissure information. The image is preprocessed and normalized before being input into the model.
2. The encoder extracts multi-scale features from the image using several convolution and pooling layers. The process includes the following steps: Conv1 and Pool1: After extracting the base texture features, the output size is reduced to 128 \(\times\) 128 after the pooling operation. Conv2 and Pool2: Deeper features are then extracted to capture the local properties of the nodes, giving an output size of 64 \(\times\) 64. Conv3 and Pool3: Higher-level features, like the overall shape and distribution of nodules, are then extracted, resulting in an output size of 32 \(\times\) 32. Conv4 and ASPP: At the final layer of the encoder, a 16 \(\times\) 16 feature map is created by combining multi-scale context using a void space pyramid pooling module (ASPP).  
The ASPP module expands the receptive field by using convolutions with different dilation rates. This helps the model capture nodal features at various scales, improving its ability to recognize both small and large nodules.
3. The decoder gradually upsamples the feature map and combines it with the encoder's features using jump connections. This process helps restore the spatial resolution of the segmentation result. The decoder's procedure is as follows: Deconv1: The feature map is upsampled from 16 \(\times\) 16 to 32 \(\times\) 32 and combined with the matching 32 \(\times\) 32 feature maps from the encoder layers. Deconv2: The feature map is upsampled from 32 \(\times\) 32 to 64 \(\times\) 64 and combined with the matching 64 \(\times\) 64 feature maps from the encoder. Deconv3: The feature map is upsampled from 64 \(\times\) 64 to 128 \(\times\) 128 and combined with the matching 128 \(\times\) 128 feature maps from the encoder. Deconv4: The feature map is upsampled from 128 \(\times\) 128 to 224 \(\times\) 224, and the initial layer feature maps from the encoder are combined to preserve more edge details. Finally, the decoder creates a binary segmentation map, matching the size of the input image, using a 1 \(\times\) 1 convolution. This map shows the locations of the nodules.
4. The segmentation results are output at the pixel level. Each pixel is classified as either a nodal region (target) in white or a non-nodal region (background) in black.