# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 1 Introduction  
With the rapid development in the field of deep learning, significant progress has been made in target detection techniques. Many advanced detectors based on CNN and Transformer [1; 2] have driven the steady development of the field. Among them, FPN [3], as a simple and effective method, improves detector performance by passing semantic information and implementing a hierarchy of CNN features. In recent years, the Transformer technique has gradually gained attention and has been applied to many target-based detection methods [4; 5; 6; 7; 8]. Compared with traditional CNN-based detectors [9; 10], these Transformer-based detectors have achieved remarkable results. However, FPN structures and Transformer-based detectors still have design flaws that affect model performance, as described below:  
**Loss of texture information in FPNs. ** The classical FPN network significantly improves the performance of the detection network through the learning of multi-scale features. Subsequent studies [11; 12; 13; 14; 15] have used a similar structure. Several studies have shown that low-level features are helpful for identifying larger targets, and the rich texture information contained in low-level features aids in target localization and accurate bounding box generation. However, features obtained from the backbone network inevitably lose a large amount of texture information during the downsampling process, which may affect the detection network's ability to accurately acquire location information. **Confounding effect of cross-level feature fusion**. When performing cross-layer feature fusion, the up-sampled feature maps are superimposed with the original feature maps, leading to feature discontinuities and confusion in the fused features, referred to as the blending effect of features [3]. The severity of the blending effect increases as more feature maps are superimposed. **The limitation of interaction between windows in Shifted Window based Self-Attention**. The global attention mechanism in Transformer-based detection networks introduces a significant computational burden [16]. Although Shifted Window-based Self-Attention reduces the computational complexity, interactions between windows are limited to neighboring windows, potentially weakening the model's perception of larger objects. To address these issues, we proposed TIG-DETR(Texturized Instance Guidance DETR), which includes a backbone network, a novel FPN (Texture-Enhanced FPN), and a detection module based on DETR. TIG-DETR aims to mitigate the aforementioned flaws and improve model performance. Our contributions:  
1. We introduced a novel approach to address the issue of missing texture information in FPN by constructing a new bottom-up path that utilizes the low-level feature map in the backbone. Unlike the traditional downsampling process in the backbone network, our method aims to preserve textural information as much as possible by constructing a new bottom-up path. By fusing this texture-rich feature map with the features at the same level as the top-down path in FPN, we obtained a new feature map that contains both rich semantic and texture information. Although previous studies [12; 17; 18] have utilized bottom-up paths, their convolution-based downsampling still results in significant texture information loss. In contrast, our method effectively retains the texture information in the feature map. 2. We proposed a new attention module called 'Feature-wise Attention' to mitigate the confounding effects caused by cross-level feature fusion in FPN. This attention module was designed to be lightweight and was applied to augment the final features of the TE-FPN output. By incorporating this attention mechanism, we enhanced the discriminative power of the features. 3. We introduced the Instance-Based Advanced Guidance Module to overcome the limitations of interactions between windows in Shifted Window-based Self-Attention [16].