# An Improved Small Target Segmentation Model Based on Mask Dino
## 1 Introduction  
Semantic segmentation is the task of classifying each pixel, while instance-level segmentation handles each instance by predicting associated binary masks. The work on fully convolutional networks (FCN) for semantic segmentation by [PERSON] et al. [1] applies classification loss to each output pixel. [PERSON] et al. [2] argue that the mask classification method is sufficiently general to uniformly handle both semantic and instance segmentation tasks. They proposed the MaskFormer model, which combines the mask method, DETR model, and Transformer architecture. Through mask-based prediction, it can effectively address complex object interactions in images, allowing the same model to be applied to both semantic and instance segmentation without modification. Nevertheless, the MaskFormer model faces challenges such as requiring up to 300 epochs of training time and significant GPU memory and computational resource overhead.  
[PERSON] et al. [2] proposed the Mask2 Former model [3, 4] to address issues related to training time and cost, demonstrating superior performance over the best specialized segmentation architectures of its time on four popular datasets. The Mask2 Former [5] architecture is concise, consisting of a backbone feature extractor, a pixel decoder, and a Transformer decoder. In the Transformer decoder, a masked attention mechanism is employed, focusing only on local features centered around the predicted segment. This approach converges faster and improves performance compared to traditional cross-attention mechanisms. Additionally, the Mask2 Former model enhances the segmentation performance for small objects and regions without additional computation by using multi-scale high-resolution features, optimizing query feature learning, and removing Dropout.  
However, [PERSON] [6] et al. pointed out that there is a lack of unity between Transformer-based segmentation models and detection models, which hinders data and task cooperation between detection and segmentation tasks. To address this issue, they proposed the Mask DINO model, which adds a mask prediction branch to the DINO [7] framework, utilizing high-resolution pixel-embedding maps to predict mask sets. However, the Mask DINO model often suffers from background information complexity during segmentation, resulting in blurred segmentation boundaries and low accuracy for small targets. Additionally, when fusing features at different scales [8], the current feature fusion strategy introduces unnecessary features, leading to information redundancy and insufficient attention to small targets. To address these issues, this paper improves the Mask DINO model using an improved backbone network attention algorithm [9], adjusting the network structure, and enhancing the multi-scale network's feature fusion strategy.  
In the field of image processing, small targets refer that are relatively small in size and limited in area. There are two main ways to define them: one is based on relative scale, such as when the ratio of the target's bounding box to the width and height of the image is less than 0.1, or the square root of the ratio of the target's area to the image's area is less than 0.03; the other is based on absolute scale, for example, in the MS COCO dataset, small targets are defined as those with a resolution of less than 32 \(\times\) 32 pixels, while in the aerial image dataset DOTA, the pixel range of small targets is from 10 to 50 pixels. Small target detection faces numerous challenges, primarily because small targets have insufficient appearance information, are easily submerged by background noise, and tend to lose details during the feature extraction process.