# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 1 Introduction  
With the rapid development of socio-economics and technology, as well as the wide-spread urbanization worldwide, road accidents and traffic congestion have become common challenges around the world. Confronted with increasingly intricate traffic environments, traditional methods relying on increasing manpower for management and scheduling are no longer able to meet the demands of current transportation systems for safety, efficiency, and environmental sustainability. Intelligent transportation systems have incorporated technologies such as the Internet of Things, artificial intelligence, cloud computing, edge computing, and automation to provide traffic information services based on real-time traffic data. Systems are being applied in areas such as smart highways, transportation data management, and autonomous driving [1]. Artificial Intelligence (AI) has been widely applied in intelligent transportation systems due to the ability of reducing human involvement while maintaining high accuracy. Vehicles and pedestrians are integral elements of the complex and dynamic road environment in urban traffic networks. The raw data for intelligent transportation is derived from semantic segmentation and object detection specifically tailored for smart transportation. The respective trajectories of vehicles and pedestrians can be obtained by segmentation and detection methods, enabling the inference of potential safety hazards [2]. Images contain a wealth of underlying semantic information, and computer vision, as a crucial technology in intelligent transportation systems, utilizes methods centered around computer vision to aid intelligent vehicles in understanding scene semantics [3]. Existing algorithms [4; 5; 6; 7] can independently achieve scene analysis in complex scenarios through semantic segmentation and object detection. Although these methods demonstrate excellent performance in their respective single task, they require sequential processing, leading to unnecessary time consumption. However, when deployingenvironment perception systems on embedded devices in driving vehicles in intelligent transportation system, limitations in computation resources and the requirement for low latency must be taken into account. Furthermore, there are often a multitude of interrelated pieces of information between multiple tasks in traffic lane scene analysis. For example, in a driving lane scene, lane markings often serve as boundaries for drivable areas, and there are typically scattered road vehicles (cars, motorcycles, etc.) and passing pedestrians around the driving area. Detecting these objects would facilitate semantic segmentation. Therefore, sharing detection information from multiple tasks contributes to enhancing the overall performance of autonomous driving perception systems. Integrating the requirements of multiple tasks into a unified model in autonomous driving scenes allows for effective information sharing among tasks, thereby improving the overall performance of autonomous driving perception systems. Moreover, in practical applications such as autonomous vehicles and traffic control, models not only need to demonstrate good accuracy but also meet the requirements of computational efficiency and real-time performance, which are crucial evaluation metrics. The depth of the network places demanding requirements on hardware and software resources, such as computational capacity and storage. Simply reducing the model size would lead to a significant decline in algorithm performance. Deep learning-based algorithms for scene understanding, including image semantic segmentation and object detection, are the main focus of this work. The goal is to achieve segmentation predictions on traffic lane datasets to aid in the analysis of road conditions. A lightweight network based on the Swin-Transformer [8] is designed, along with an adapter network suitable for both image semantic segmentation and object detection tasks.