## 2 Materials and Methods
### Details Structure of LightExNet
#### 2.2.1 Overall Network Architecture
At the same time, the first shallow feature map \(\mathbf{F}_{s1}\) from the AuxSTAGE1 module is processed by the AuxSTAGE2 module to form the second shallow feature map \(\mathbf{F}_{s2}\in\mathbf{R}^{8\times 28\times 28}\) with dimensions of 8 \(\times\) 28 \(\times\) 28. The second deep feature map \(\mathbf{F}_{d2}\) and the second shallow feature map \(\mathbf{F}_{s2}\) are cascaded in the channel dimension to form the second deep and shallow fusion feature map \(\mathbf{F}_{sd2}\in\mathbf{R}^{23\times 28\times 28}\) with dimensions of 32 \(\times\) 28 \(\times\) 28. Then this feature matrix is processed by \"Attention Process\" module, and finally a feature matrix with size of 32 \(\times\) 28 \(\times\) 28 will be output. * STAGE3: Following the Attention Process module, the output is a feature matrix of size 32 \(\times\) 28 \(\times\) 28, which is firstly input into the BTNK2 module (with the parameters of 32, 28, 6, 48) for processing to form an output feature matrix of size 48 \(\times\) 14 \(\times\) 14, and then input into the BTNK1 module (with the parameters of 48, 14, 6, 48) for processing to form an output feature matrix with the size of 48 \(\times\) 14 \(\times\) 14, and then input the data to an identical BTNK1 module (with parameters 48, 14, 6, 48) for processing to form an output feature matrix with the size of 48 \(\times\) 14 \(\times\) 14 in the end, which is the third deep feature map \(\mathbf{F}_{\text{d3}}\in\mathbf{R}^{48\times 14\times 14}\). At the same time, the second shallow feature map \(\mathbf{F}_{\text{s2}}\) from AuxSTAGE2 module is processed by AuxSTAGE3 module to form the third shallow feature map \(\mathbf{F}_{\text{s3}}\in\mathbf{R}^{16\times 14\times 14}\) with the size of 16 \(\times\) 14 \(\times\) 14. The third deep feature map \(\mathbf{F}_{\text{d3}}\) and the third shallow feature map \(\mathbf{F}_{\text{s3}}\) are cascaded in the channel dimension to form the third deep and shallow fusion feature map \(\mathbf{F}_{\text{s3}}\in\mathbf{R}^{64\times 14\times 14}\) with the size of 64 \(\times\) 14 \(\times\) 14. Then this feature matrix will be processed by \"Attention Process\" module, and the final feature matrix with the size of 64 \(\times\) 14 \(\times\) 14 will be output. * STAGE4: Following the Attention Process module, the output is a feature matrix with the size of 64 \(\times\) 14 \(\times\) 14, which is first input into the BTNK1 module (with the parameters of 64, 14, 6, 96) for processing to form an output feature matrix with the size of 96 \(\times\) 14 \(\times\) 14, and then input into another BTNK1 module (with the parameters of 96, 14, 6, 96) for processing to form an output feature matrix with the size of 96 \(\times\) 14 \(\times\) 14, and another identical BTNK1 module (with parameters 96, 14, 6, 96) is input for processing to form another output feature matrix with the same dimensions 96 \(\times\) 14 \(\times\) 14. Following input the data to another BTNK2 module (with parameters 96, 14, 6, 120) for processing, forming an output feature matrix with the size of 120 \(\times\) 7 \(\times\) 7, and then another BTNK1 module (with parameters 120, 7, 6, 120) for processing, finally forming an output feature matrix with the size of 120 \(\times\) 7 \(\times\) 7, this is the fourth deep feature map \(\mathbf{F}_{\text{d4}}\in\mathbf{R}^{120\times 7\times 7}\). At the same time, the third shallow feature map \(\mathbf{F}_{\text{s3}}\) from AuxSTAGE3 module is processed by AuxSTAGE4 module to form the fourth shallow feature map \(\mathbf{F}_{\text{s4}}\in\mathbf{R}^{40\times 7\times 7}\) with the size of 40 \(\times\) 7 \(\times\) 7. The fourth deep feature map \(\mathbf{F}_{\text{d4}}\) and the fourth shallow \(\mathbf{F}_{\text{s4}}\) feature map are cascaded in the channel dimension to form the fourth deep and shallow fusion feature map \(\mathbf{F}_{\text{s4}\text{d}}\in\mathbf{R}^{160\times 7\times 7}\) with the size of 160 \(\times\) 7 \(\times\) 7.