## 1 Introduction  
As an important part of railway transportation, high-speed railway not only has the advantages of convenience, safety, and affordability, but also has become one of the most important methods of mass travel due to its fast and comfortable characteristics [1]. The reliable operation of high-speed rail pantographs determines the safety of railway transportation to a large extent. In order to ensure that trains maintain normal operation and detect pantograph faults in a timely manner, abnormal pantograph faults must be detected quickly and accurately [2; 3; 4; 5; 6]. The pantograph slide monitoring device is installed on stations, at the throat areas of stations, on high-speed trains, and in locomotive de-pots for electric locomotives or high-speed railways. It uses high-speed, high-resolution, non-contact image analysis and measurement technology, and other methods, to achieve automatic detection monitoring of the status of the pantograph slide and the visual observation of foreign objects on the roof, as well as the status of key components [7; 8; 9].  
At present, the defect recognition method for pantograph slides based on image processing has achieved some results and applications in pantograph sliding state monitoring devices. Commonly used pantograph positioning methods include Hough transform, the HOG feature, edge detection, pattern matching, second-generation curve wavelet transform, spatial region reconstruction, and use of the density clustering algorithm. However, these traditional image processing methods are susceptible to changes in lighting and noise interference, and their recognition ability in complex backgrounds needs to be strengthened [10; 11]. For example, [PERSON] et al. [12] designed an R-CNN model using convolutional neural networks, replacing the traditional graphic feature extraction part with AlexNet, and combined it with a selective search method for generating region proposals. In the object detection task of the PASCAL VOC 2012 data set, they achieved a mean average precision (mAP) of 62.4%, which is 20% higher than traditional object detection methods. [PERSON] et al. (2013) designed a pyramid pooling layer after the last convolutional layer of SPPNet to unify the size of feature images without considering the size of the input image. The experiment showed that SPPNet's testing time was 24-102 times faster than R-CNN. The Faster R-CNN network proposed for the first time by [PERSON] et al. (2014) adopts the Region Proposal Network (RPN), which can complete both candidate region generation and region classification tasks, thereby reducing the computational time overhead caused by selective search algorithms and achieving end-to-end training, solving the problems of R-CNN time and space overhead.  
With the rapid development of deep learning technology and big data analysis technology in recent years, combined with deep learning and traditional image processing methods, this paper proposes an improved intelligent recognition method for pantograph positioning and skateboard structure anomalies and defect recognition based on the YOLO v8 algorithm. This chapter first analyzes the two-stage YOLO v8 algorithm network model as the basic structure of the object detection algorithm, and then improves the network structure based on the complex background and large target characteristics of the pantograph image, ultimately achieving the intelligent recognition function of the pantograph. The specific YOLO v8 network model is described in detail in Chapter 2.