Abstract: Aiming at the problems of low efficiency, false detection and missed detection in traditional vehicle appearance damage detection, this paper proposes an efficient data acquisition and preprocessing technology solution based on machine vision. Firstly, a complete machine vision acquisition framework is designed and built, including hardware devices such as industrial cameras, light sources and camera brackets, to comprehensively collect damage images of different parts of the vehicle appearance. Through field research in 4S stores, used car markets, vehicle inspection stations and other places, the types of damage are enriched, and the diversity and reliability of image data are improved. For the collected images, this paper utilized software such as Python and OpenCV for preprocessing, including image grayscale conversion, noise removal, and highlighting of damaged areas. Meanwhile, the damaged images were labeled using the labelimg software to build a high-quality dataset. By integrating tools like Machine Vision Studio （MVS）, SDK, and Qt Framework, seamless integration between the camera and third-party platforms was achieved, simplifying operation steps and enhancing work efficiency. Through data cleaning and enhancement of the collected images, and combined with on-site testing, the feasibility and effectiveness of this method in practical applications have been verified. This design provides an efficient data collection and preprocessing solution for vehicle exterior damage detection systems, and lays a reliable data foundation for subsequent damage recognition and detection based on deep learning.