# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
## 1 Introduction  
Traffic safety [1] has always been the focus of the world's attention and one of the main problems solved by each country. In recent years, research institutes, automobile companies, and other industries have been conducting in-depth research on intelligent driving, which is typically represented by Advanced Driver Assistance Systems (ADAS) [2; 3], which can monitor the road and traffic environment, as well as the state of the vehicle itself, and then help drivers prevent accidents and improve driving safety through warnings, interventions, or autonomy control. The first task for ADAS is obstacle avoidance, but the prerequisite for obstacle avoidance is the accurate detection of obstacles. At present, although there are many different methods for target detection, there are various shortcomings, such as: missed detection, false detection, lack of real time, and so on. According to the research, it is found that the current environment perception of ADAS systems mainly has the application based on a radar point cloud [4] and the application based on pure vision [5]. Although radar technology has a long measuring distance, the difficulties of not being able to obtain visual information, the high cost, and the difficulty in distinguishing between multiple targets have been the difficulties of research in various industries. For visual perception, although limited by the complex environment, a high resolution can provide rich target information and is favored in the car industry due to its low cost and other advantages. The typical representative among them is the 2023 Tesla FSD V12, which is an end-to-endpure visual perception system. The detection of obstacles is one of the key research focuses of the environment perception system, and the obstacle detection methods are mainly divided into traditional visual detection and deep learning-based target detection. With the rapid development of computer vision technology and deep learning algorithms, obstacle detection has become an important technology in assisted driving systems [6]. Currently, deep learning-based obstacle detection methods have become mainstream, and the commonly used architectures are R-CNN [7], Fast R-CNN [8], Faster R-CNN [9], mask R-CNN [10], and YOLO [11]. Among them, the YOLO series of algorithms performs better in terms of speed and accuracy and is one of the most widely used obstacle detection algorithms. However, in practical applications, pole-shaped obstacles are susceptible to environmental factors such as lighting, weather conditions, and occlusion. These factors can significantly impact the accuracy of detection and the range of pole-shaped obstacles. Moreover, due to the different heights and shapes of pole-shaped obstacles, multi-scale detection is required to better detect pole-shaped obstacles of different sizes and shapes. [PERSON] et al. [12] proposed a method for the rapid detection of traffic cones. First, an improved detector is used to detect the traffic cones. Then, a regression network is employed to identify the key features of the traffic cones. Finally, the three-dimensional information of the traffic cones is obtained using the perspective n-point algorithm. Although this method can run on low-power hardware, there is still significant room for improvement in terms of detection speed and accuracy. [PERSON] et al. [13] propose a feature fusion method aimed at improving obstacle detection performance under foggy conditions. The main idea is to identify the differences in image features between sunny and foggy days, construct a dataset of foggy images, and then utilize the feature fusion method to enhance obstacle detection performance. [PERSON] et al. [14] proposed a method of fusing convolutional features and the GCANet network for the problem of the difficult extraction of obstacle feature information under a foggy sky. After processing the obstacle images of the pair under foggy weather, the information on the original obstacles is fully retained, thus realizing the detection of obstacles under the foggy sky.