## 1 Introduction
The second step involves locating and classifying the data using a convolutional neural network with high detection accuracy, such as R-CNN [14], Fast R-CNN [15], Faster R-CNN [16], SPPNet [17], etc. In contrast to two-stage algorithms, which require candidate regions to be generated before positioning and classification can be performed, one-stage detection algorithms perform both operations simultaneously. The detection speed of algorithms such as YOLO [18], SSD [19], RetinaNet [20], and FCOS [21] is better than that of the two-stage algorithm, but the accuracy is lower. YOLOv5 is an object detection algorithm that belongs to the YOLO (You Only Look Once) family of algorithms. It was released in June 2020 and quickly gained attention in the computer vision community due to its improved performance in speed, accuracy, and model size [22]. In terms of speed, YOLOv5 can detect objects in real time or near real time, meaning it can process a video stream frame by frame and return object detections almost instantly. The combination of fast speed and high accuracy makes YOLOv5 a popular choice for object detection tasks in various fields, including surveillance [23], robotics [24], and autonomous driving [25]. In these applications, quick and accurate object detection is crucial for the success of the system. In terms of fast object detection systems, Huawei's Noah's Ark Lab proposed a new end-to-end neural network architecture called GhostNet, which aimed to improve the detection speed. This architecture was included in CVPR2020 [26]. The GhostNet paper proposed a Ghost module that could generate more feature maps from cheaper operations than conventional convolution operations, thereby reducing the number of floating-point operations (FLOPs) in the network. The experimental results showed that the proposed Ghost module decreased the computational cost of general convolutional layers while maintaining similar recognition performance. Fast object detection and high accuracy are important factors to consider when developing a bus detection model, especially for people with visual impairment. A lightweight and high-accuracy model is desirable to ensure real-time performance and ease of use on mobile devices. To address this issue, this study proposes a bus detection model that is lightweight, accurate, and suitable for use on mobile devices. The proposed model aims to provide an efficient and reliable solution for individuals with visual impairment to detect and recognize buses in real time. By combining YOLOv5 and GhostNet, we can offer a promising solution for real-time bus detection. Our solution leverages YOLOv5's high speed and accuracy and GhostNet's ability to reduce computational costs. This combination can efficiently detect buses in real-time video streams from different angles and distances. Furthermore, our solution addresses the computational challenges associated with object detection by using GhostNet to reduce the computational burden of YOLOv5. This enables our solution to be deployed on resource-constrained devices, such as mobile phones or embedded systems. As a result, we can implement real-time bus detection on edge computing systems, without relying on cloud computing resources. Moreover, the SimSPFF module is added to the YOLOv5 backbone to enhance the computational efficiency and object detection capabilities. The Slim scale detection model is also developed by modifying the original YOLOv5 structure, making it more efficient and faster for real-time object detection applications. In summary, our proposed combination has the potential to provide an efficient and accurate solution for real-time bus detection for individuals with visual impairments, as real-time bus detection can aid in navigation and improve accessibility for those with disabilities. Additionally, our solution can also be beneficial for smart city transportation systems and autonomous driving.