## 1 Introduction  
Object recognition and detection has been an important problem for a long time for many different industries, including the medical field, the security industry, and the transportation sector. This issue, however, takes on a greater level of significance for people who are blind or have some other form of visual impairment. According to estimates by the World Health Organization (WHO), at least 2.2 billion people globally suffer from some form of near or distance vision impairment, with associated annual productivity losses of around $411 billion [1]. People who are visually impaired face a wide variety of difficulties on a daily basis. These challenges can range from difficulties with mobility and orientation to difficulties accessing information and services. One of the most significant issues that individuals with visual impairment face on a daily basis is related to the use of public transportation, with the difficulty of getting to their destination, followed by the inconvenience and then safety concerns [2]. In the context of public bus transportation, people with visual impairment require information about their surroundings and any visible details at bus stops and terminals, such as schedules and routes, in order to use the system independently and safely. However, the majority of individuals with visual impairment face challenges when it comes to taking the correct bus and disembarking at the correct destination [3]. As a result, they may have to rely on others for transportation, limit their travel, or choose simpler activities that do not require extensive travel. In recent years, the advancement of technology has enabled the development of more accurate and efficient object detection and recognition systems. Therefore, bus detection has been an area of active research. RFID and embedded systems are widely used for bus detection in intelligent transportation systems. These systems use RFID tags embedded in buses to communicate with stationary RFID readers at bus stops, enabling real-time bus detection and location tracking [4; 5; 6; 7]. For instance, a study by [PERSON] et al. [8] proposed an RFID-based bus detection system using passive RFID tags and RFID readers at bus stops. Another study proposed a mechatronic system architecture to improve transportation for people with visual impairment by combining RFID and wireless sensor networks [9]. However, RFID and embedded systems also have their limitations. These systems require specific hardware and can be expensive to install and maintain [10]. They can also be affected by environmental factors such as interference from nearby metal objects, signal reflections, and other sources of noise [11]. However, deep learning-based methods, such as those using CNNs and object detection models, are becoming increasingly popular for object detection due to their high accuracy and real-time performance. These methods can accurately detect and recognize buses in real-world scenarios, even in complex urban environments. They can also be used on mobile devices, making them accessible to individuals with visual impairment. For example, [PERSON] et al. [12] introduced a deep learning model, a lightweight and energy-efficient transportation model detection system, using only accelerometer sensors in smartphones. Another study conducted a comparative study to evaluate the effectiveness of two deep learning models, AlexNet and Faster R-CNN, for detecting vehicles in an urban video sequence, which produced positive results, and the tests provided important insights into the architectures and strategies used to implement these models for video detection [13]. The deep-learning-based object detection algorithm has made significant progress in detection accuracy and speed. There are two types of deep learning target detection algorithms: those with two stages and those with only one. After feature extraction in the first stage, the two-stage detection algorithm will generate a region proposal that may contain the target to be detected.