# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 2 Related Work  
**FPN**. Before the introduction of FPN, various approaches for feature processing existed, including featurized image pyramids, single feature maps, and pyramidal feature hierarchy. SSD [19] utilized pyramidal feature hierarchy, specifically focusing on hierarchical feature prediction goals, to enable different level features to learn the same semantic information. FPN [3] proposes a method for fusing features of different resolutions by element-wise addition of the feature map from each resolution with the up-sampled low-resolution feature map. This enhancement improves the features at different levels and subsequent models have built upon the FPN foundation. PANet [12] introduced a bottom-up pathenhancement to shorten the information path by utilizing the precise localization signal stored in low-level features, thereby improving the performance of the feature pyramid architecture. EfficientDet [9] borrowed the TopDown-BottomUp concept from PANet and incorporated residual structures in each block to reduce optimization difficulties. Furthermore, the authors recognized that features from different layers possess varying semantic information. Directly summing these features can lead to sub-optimal problems. To address this, the authors introduced a learnable parameter in front of each layer of features to automatically determine their weights. Aug-FPN [13] proposes Soft ROI Selection, which involves pooling ROI features from different levels and fusing them to enhance the performance of the feature pyramid architecture. To mitigate the loss of textural information in high-level feature maps, Aug-FPN incorporates a residual enhancement branch specifically designed to enhance the texture information of these high-level feature maps. In CE-FPN [14], sub-pixel enhancement and attention-guided modules were employed in FPN to fully leverage the rich channel information of each level feature map, while minimizing the loss of channel information during the downscaling process. **Target detector**. Traditional image target detection can be categorized into two main types: two-stage detectors, with Faster R-CNN [20] being the most representative example, and one-stage detectors such as YOLO [21], YOLO9000 [22], and YOLOV3 [23]. R-CNN [24] demonstrated for the first time the significant improvement in target detection performance by using CNN on the PASCAL VOC dataset [25] compared to HOG-like feature-based systems. Fast R-CNN [26], proposed subsequently, overcomes the time-consuming aspect of R-CNN's SVMs [27] classification by employing ConvNet forward propagation for each region without redundant computation. Fast R-CNN [28] extracts features from the entire input image and passes them through the ROI pooling layer to obtain fixed-size features for subsequent classification and bounding box regression in the fully connected layer. Instead of extracting features for each region separately, Fast R-CNN extracts features once from the entire image, reducing both the processing time and the storage space required for a large number of features. Fast R-CNN [26] adopts selective search to propose RoIs but this approach is slower and has the same running time as the detection network. In contrast, Faster R-CNN [20] introduces a new RPN (region proposal network) that is composed entirely of convolutional networks and efficiently predicts region proposals. The RPN shares the same set of common convolutional layers with the detection network, and the fully convolutional Mask R-CNN [28] optimizes the model by integrating low-level and high-level features to enhance the classification task. YOLO [21] pioneered one-stage target detection, and subsequent one-stage detectors have built upon its improvements. Generally, two-stage detectors achieve higher localization and target detection accuracy, while one-stage detectors offer faster inference speed.