[43] in 2020 in the paper \"An Image Is Worth 16 \(\times\) 16 Words: Transformers for Image Recognition\". This study explores the application of the Transformer architecture in image classification tasks, introducing a novel approach of slicing images into fixed-sized patches and feeding them into a Transformer model for processing, instead of using a traditional convolutional neural network (CNN). The research demonstrates that the Transformer architecture can achieve comparable performance to a traditional CNN in image classification, laying the foundation for the development and subsequent applications of ViT models in the field of image processing. DETR (Detection Transformer), proposed by [PERSON] et al. [44] in 2020, is a model for end-to-end object detection using the Transformer architecture. The DETR model eliminates the need for traditional components, such as anchor boxes and non-maximum suppression (NMS), in conventional object detection algorithms by leveraging attention mechanisms, directly encoding and predicting object positions using Transformers. Deformable DETR, introduced by [PERSON] et al. [45] in 2020, integrates deformable mechanisms into the Transformer model, enabling the model to flexibly adjust receptive fields to adapt to objects of different scales and shapes, thereby better capturing and understanding the actual shapes and positions of objects in images. Conditional DETR, proposed by [PERSON] et al. [46] in 2021, learns conditional space queries from decoder embeddings for decoder multi-head cross-attention. This approach reduces the spatial range for different regions used for target classification and box regression, thereby reducing reliance on content embeddings and simplifying the training process. Real-Time Detection Transformer (RTDETR), introduced by [PERSON] et al. [47] in 2023, designs an efficient hybrid encoder to effectively handle multi-scale features by decoupling intra-scale interaction and cross-scale fusion. Additionally, it proposes IoU-aware query selection to improve the initialization of target queries. Moreover, the proposed detector supports flexible adjustment of inference speed using different de -color layers without the need for retraining, facilitating practical applications of real-time object detection. In addition to the object detection methods mentioned earlier, several innovative approaches have been proposed in the research community. [PERSON] et al. [48] in 2015 introduced Faster R-CNN, which incorporated a region proposal network (RPN) for swift feature extraction and object localization. [PERSON] et al. [49] in 2016 streamlined the process with the Single Shot MultiBox Detector (SSD) by enabling direct object classification and localization across multiple feature maps. [PERSON] et al. [50] in 2017 enhanced SSD with the Deconvolutional Single Shot Detector (DSSD), using deconvolutional layers to improve feature map resolution. Introduced by [PERSON] and [PERSON] [51] in 2018, Cascade Mask R-CNN is a high-performance framework for object detection and instance segmentation. It improves on Mask R-CNN by incorporating a cascading strategy, enhancing performance in handling small objects or objects within complex backgrounds. [PERSON] et al. [52] in 2020 presented adaptive training sample selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of objects to improve the performance of anchor-based and anchor-free detectors. [PERSON] et al. [53] in 2020 introduced RepPointsV2, capturing nuanced object shapes. [PERSON] et al. [54] in 2021 revolutionized object detection with Sparse R-CNN, simplifying detection processes with learned proposals. In the field of object detection based on visual data, the clarity of texture and distinct color contrast in images is crucial for learning visual features for accurate prediction.