Railway catenary contact wires are safety-critical and availability-critical components whose surface defects may affect current collection quality, electrical conductivity, and operational safety. While computer vision for railway overhead line equipment has advanced the field of rail engineering, existing studies have focused mainly on defect detection in catenary components, whereas dedicated vision-based deep learning studies on surface crack analysis of the contact wire remain limited. This project investigates a deep learning framework for surface crack analysis in railway catenary contact wires using image data extracted from 4K laboratory bending-test videos. We have employed a bending machine to deform contact-wire workpieces and simulate defects in railway contact wires; therefore, we have collected large experimental image datasets from the experiments, including a large number of images of non-defective workpieces and a smaller number of images of defective workpieces. In this project, we plan to perform the workflow, including crack segmentation, crack detection, synthetic data generation, crack size measurement and prototype-level deployment validation. Eventually, our final objective is to deploy the trained AI-based crack detection model in a camera for real-time railway catenary detection in contact wire. In implementation, we will do the following: 1. A U-Net-based model will first be trained as a pixel-level segmentation baseline for crack delineation. Based on the corresponding crack masks or derived candidate regions, a lightweight detection model will then be developed for crack detection. 2. A diffusion-based generative model will be developed for few-shot learning, where limited real defect samples are used to generate additional crack image-mask pairs under controlled conditions. These generated data will be used to retrain and systematically study how different real-to-synthetic ratios affect downstream segmentation and detection performance. The resulting crack masks will further support geometric quantification, including crack length, width, and area. 3. Finally, selected models will be deployed to a development board for prototype-level validation under practical inference constraints. The expected outcome is a precise estimation for railway catenary contact wires segmentation, detection, and quantitative assessment for inspection.