# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 2 Related Work  
Semantic segmentation has been widely researched and applied in traffic lane scene analysis. [PERSON] [4] proposed a feedback-based deep semantic segmentation method that can integrate spatial context by incorporating an additional output feedback mechanism, eliminating the need for post-processing steps such as Conditional Random Fields (CRF) refinement. [PERSON]. [5] introduced a shallow Convolutional Neural Network (CNN) called Multi-View Sampling CNN (MVS-CNN), which utilizes abstract features extracted from the gradient information of images to improve the semantic segmentation of road areas. In order to capture and convey road-specific contextual information, [PERSON]. [9] focused on the Spatial Information Inference Structure (SIIS), which can learn both local visual features of roads and global spatial structural information. To accurately extract linear features, a novel Dilated Vertical and Horizontal kernel (DVH) was introduced into the feature extraction task of the semantic segmentation network [6]. Mobile Laser Scanning (MLS) technology has also been widely used for road image segmentation and recognition [10]. By analyzing high-resolution images, a better understanding of urban traffic conditions can be achieved, facilitating the formulation of traffic policies and infrastructure investment plans. However, in practical application scenarios, autonomous driving has strict requirements for real-time road and obstacle detection methods. Therefore, there is a need to develop novel semantic segmentation models to ensure the accuracy of various indicators for autonomous vehicles.  
Object detection is another key task in intelligent transportation. It can be used for real-time monitoring of traffic flow and for identifying and tracking vehicles and pedestrians on the road. Object detection methods based on CNN (Convolutional Neural Networks) delve into deeper features, providing more sensitive generalization and adaptability. They overcome the challenges posed by complex outdoor traffic environments and variations in vehicle scales, among other uncertain factors. YOLO series [11; 12; 13] were widely used in vehicle detection and related tasks.  
[PERSON] et al. [14] proposed an improved Tiny-YOLOv3 model that can detect and classify objects at a speed of 95 FPS (Frames Per Second) on the BIT vehicle dataset by pruning and simplifying the model. [PERSON] et al. [7] added a \(104\times 104\) detection layer to YOLOv3 and recalculated 12 anchor boxes using k-means to improve the detection accuracy of small vehicles. [PERSON] et al. [15] proposed a multi-scale vehicle detection method based on Spatial Pyramid Pooling, which enhanced robustness against vehicle occlusion and scale variation. YOLOv4 is an upgraded version of YOLOv3. [PERSON]. [16] introduced an adaptive model by combining YOLOv4 with Deepsort, enabling real-time detection and counting of various types of vehicles, which achieved improved detection accuracy at a speed of 32 FPS. [PERSON]. [17] proposed a real-time traffic monitoring system based on virtual detection zones, Gaussian Mixture Models, and YOLO. This system aimed to enhance vehicle counting and classification efficiency. These innovations and improvements in object detection methods contribute to the development of efficient and accurate systems for traffic monitoring and management in intelligent transportation applications.  
Based on the aforementioned, [PERSON]. [18] proposed a shared encoder with three independent decoder architectures to simultaneously accomplish scene classification, object detection, and drivable area segmentation tasks. The network performed well in multiple tasks but did not incorporate lane line detection. [PERSON]. [19] also employed an encoder-decoder structure and built context tensors between the subtask decoders to facilitate the sharing of specific information among tasks. These algorithms utilize an encoder to extract features and decoders to perform individual tasks, making the network relatively complex.