Electrodes are critical components of lithium-ion batteries, and during manufacturing processes like coating and rolling, their surfaces are susceptible to defects such as scratches and foil exposure. These defects can significantly impact the quality and service life of the batteries. Consequently, defect detection and control procedures for battery electrodes are essential steps in lithium-ion battery production. This article begins by outlining the production process of lithium-ion battery electrodes and analyzing the potential causes and types of surface defects that can occur during manufacturing. Next, it discusses the use of machine vision for surface defect identification, replacing manual labor with automated detection. The article reviews the principles, advantages, and limitations of traditional machine vision defect detection methods. It then delves into the application of deep learning for electrode surface defect detection, focusing on the principles and procedures involved. Particular attention is given to comparing one-stage and two-stage algorithms in target detection for lithium-ion battery electrode defect detection. Finally, the article predicts future developments in machine vision detection methods based on deep learning for surface defect detection of lithium-ion battery electrodes, offering valuable insights for researchers in this field. The advancement of electrode surface defect detection technology depends not only on hardware innovations, such as industrial cameras, but also on continuous optimization and innovation of software algorithms. The synergy between software and hardware can enhance detection accuracy, improve efficiency, reduce costs, and drive the lithium-ion battery production industry toward high-quality development.