Abstract: Surface defect detection of electronic components is a critical step in the manufacturing process of electronic products, playing a vital role in ensuring the quality and reliability of the final assembled systems. In recent years, computer vision technologies have been increasingly and extensively applied in the field of surface defect detection for electronic components, demonstrating significant potential and technical advantages. This paper provides a systematic review of methods utilizing computer vision for surface defect detection of electronic components. It first clarifies the visual tasks involved in the detection process and elaborates on the fundamental processing pipeline. From a technical perspective, the development of computer vision-based defect detection technologies is discussed by categorizing existing methods into three groups: image processing-based methods, traditional machine learning-based methods, and deep learning-based methods. On this basis, the component surface defect datasets constructed in this field are introduced. Finally, the current challenges faced by the field are discussed, along with potential future research directions and development trends, aiming to provide a systematic reference for researchers in related areas.