# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 3 Materials and Methods
### Te-Fpn  
The top-down propagation of robust semantic information by FPN enhances the model's ability to accurately classify features at all levels of the pyramid. The accurate localization of instances in the model relies on their high response to instance parts or edges, whereas the bottom-up path approach effectively propagates robust texture information, thereby enhancing the model's ability to localize features at all levels of the feature pyramid. In this paper, we proposed a new pyramid structure Texture-Enhanced FPN (TE-FPN), which contains a bottom-up path leading from the low level of the backbone network, so that the fused feature map has both strong semantic and textural information. Additionally, we introduced a novel channel attention mechanism to the Soft RoI Selection process, aiming to further enhance the fused features. **Enhancing textural information with a bottom-up architecture. ** FPN acquires features from the backbone and a large amount of texture information is inevitably lost when the backbone is downsampled, a situation that may affect the accuracy of the detection network in obtaining information about the location of instances in the image. To address this limitation, we incorporated an 'enhancing textural information with a bottom-up architecture' (ETA) into FPN, aiming to enhance the texture information in the feature map at each level. Following the definition of FPN [3], feature layers of the same size were generated in each network phase, and different feature layers correspond to different phases of the network. As shown in Figure 2, the Resnet-50 [45] serves as the backbone network and \(\{P2,P3,P4,P5\}\) represent the feature layers generated by the FPN. \(\{C2,C3,C4,C5\}\) represent the feature layers at different stages in the backbone network and \(\{D2,D3,D4,D5\}\) represent the feature layers of \(\{C2,C3,C4,C5\}\) after dimensionality reduction using convolution. From C2 to C5, \(P2\) to \(P5\), and \(D2\) to \(D5\) spatial sizes were gradually downsampled with a downsampling factor of 2. \(\{N2,N3,N4,N5\}\) represent the feature maps newly generated by the bottom-up path, corresponding to \(\{C2,C3,C4,C5\}\). Specifically, the first step was to reduce \(N2\) to C2 using convolution with a channel dimension of 256. This channel dimension aligns with the feature map in FPN and enables effective fusion between the two feature maps. Subsequently, the downsampled feature map was further downsampled with sampling coefficients of 2, 4, and 8 to generate \(\{N3,N4,N5\}\), preserving more texture information compared to conventional convolutional downsampling. The process is described as follows:  
\[N_{i}=pool_{2^{i-2}}(N_{2}), \tag{1}\]  
where \(pool_{\gamma}\) denotes the downsampling of the sampling factor of \(\gamma\). Finally, depicted in Figure 3a, we up-sampled Pi using a sampling factor of 2 and merged the up-sampled feature map with \(N_{i+1}\)and \(D_{i+1}\), which have the same size, to generate \(P_{i+1}\). Notably, \(P5\) was obtained by merging only \(D5\) and \(N5\). The resulting fused feature map contains a combination of rich semantic and textural information. To mitigate the confounding effect after feature fusion, we employed a Lightweight Feature-wise Attention (LFA) module, as shown in Figure 3b. In this module, we implemented a lightweight attention mechanism using FC layers instead of the more complex shared MLP  
Figure 1: TIG-DETR comprises a backbone network, a new pyramidal structure known as Texture-Enhanced FPN (TE-FPN), and an enhanced DETR detector. layers and combined the output feature vectors through element-wise summation with a sigmoid function. The process can be summarized as follows:  
\[ \begin{split} LFA(F)&=\sigma(fc1(Avgpool(x))+fc2( Maxpool(x)),\\ &+fc3(Softpool(x)))\end{split}  \tag{2}\]  
where \(LFA\) denotes the lightweight channel attention function, \(\sigma\) denotes the sigmoid function, \(Avgpool\), \(Maxpool\), \(Softpool\) denotes the global average pooling, global maximum pooling, and global soft pooling.